Multi-sensory, assistive wearable technology, and method of providing sensory relief using same

ABSTRACT

A system and method for providing sensory relief from distractibility, inattention, anxiety, fatigue, and/or sensory issues to a user in need. The user can be autistic/neurodiverse, or neurotypical. The system can be configured to obtain user sensory sensitivity data indicating a user&#39;s visual, sonic, or interoceptive, sensitivities; determine, using at least the user sensory sensitivity data, sensory thresholds specific to the user and mediation data corresponding to mediations specific to the user; store the sensory thresholds and mediation data; record, using one or more sensors, a sensory input stimulus to the user; compare the sensory input stimulus with the sensory thresholds; in response to comparing the sensory input stimulus with the sensory thresholds, determine, based at least on the mediation data, a mediation to be provided to the user, the mediation configured to provide the user relief from distractibility, inattention, anxiety, fatigue, or sensory issues.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of and claims priority toU.S. patent application Ser. No. 17/882,517, titled “MULTI-SENSORY,ASSISTIVE WEARABLE TECHNOLOGY, AND METHOD OF PROVIDING SENSORY RELIEFUSING SAME” filed Aug. 5, 2022, which claims priority to U.S.Provisional Patent Application No. 63/229,963, titled “MULTI-SENSORY,ASSISTIVE WEARABLE TECHNOLOGY, AND METHOD OF PROVIDING SENSORY RELIEFUSING SAME” filed Aug. 5, 2021, and U.S. Provisional Patent ApplicationNo. 63/238,490, titled “MULTI-SENSORY, ASSISTIVE WEARABLE TECHNOLOGY,AND METHOD OF PROVIDING SENSORY RELIEF USING SAME” filed Aug. 30, 2021.The aforementioned applications are incorporated herein by reference intheir entirety.

BACKGROUND

In this specification where a document, act or item of knowledge isreferred to or discussed, this reference or discussion is not anadmission that the document, act or item of knowledge or any combinationthereof was at the priority date, publicly available, known to thepublic, part of common general knowledge, or otherwise constitutes priorart under the applicable statutory provisions; or is known to berelevant to an attempt to solve any problem with which thisspecification is concerned.

A significantly high percentage (about 90%) of autistic adults reportthat sensory issues cause significant barriers at school and/or work.(Leekam, S. R., Nieto, C., Libby, S. J., Wing, L., & Gould, J. (2007).Describing the Sensory Abnormalities of Children and Adults with Autism.Journal of Autism and Developmental Disorders, 37(5), 894-910).Additionally, 87% of autistic employees feel environmental adjustmentswould make critical differences to their performance. (Maltz, S. (2019).Autistica Action Briefing: Employment-Harper G, Smith E, Heasman B,Remington A, Girdler S, Appleton V J, Cameron C, Fell C). These numbersmake a compelling case for addressing the sensory issues that affect anautistic adult's ability to function successfully. Environmental factorsare also known to trigger persistent sensory and cognitive challenges(e.g., sensory overload) leading to mental health challenges. Mentalhealth is the number one autistic priority and primary barrier toschooling/employment (Cusack, J., & Sterry, R. (2019, December).Autistica's top 10 research priorities), and it contributessubstantially to autism's societal expenditures, which in the UK exceeds£27.5 billion per annum—surpassing cancer, heart, stroke, and lungdiseases combined. (Knapp, M., Romeo, R., & Beecham, J. (2009). Economiccost of autism in the UK. Autism, 13(3), 317-336); (London School ofEconomics (2014). Autism is the most costly medical condition in theUK).

SUMMARY

This application addresses the above-described challenges, by providinga wearable technology that offers ground-breaking opportunities to: (i)monitor environments and adjust user-experiences; (ii) lessensensory-load and enable greater participation; and (iii) improve mentalhealth with efficacious interventions. The wearable technology describedherein increases attentional focus, reduces sensory distraction, andimproves quality-of-life/lessens anxiety and fatigue.

It should be understood that the various individual aspects and featuresof the present invention described herein can be combined with any oneor more individual aspect or feature, in any number, to form embodimentsof the present invention that are specifically contemplated andencompassed by the present invention.

One embodiment of the application is directed to a system, comprising: awearable device comprising one or more sensors; one or more processors;and one or more non-transitory computer-readable media having executableinstructions stored thereon that, when executed by the one or moreprocessors, cause the system to perform operations comprising: obtaininguser sensory sensitivity data corresponding to user input indicatingwhether a user of the wearable device is visually sensitive, sonicallysensitive, or interoceptively sensitive; determining, using at least theuser sensory sensitivity data, one or more sensory thresholds specificto the user and mediation data corresponding to one or more mediationsspecific to the user, the one or more sensory threshold selected fromauditory, visual, or physiological sensory thresholds; storing the oneor more sensory thresholds and the mediation data; recording, using theone or more sensors, a sensory input stimulus to the user; comparing thesensory input stimulus with the one or more sensory thresholds specificto the user; in response to comparing the sensory input stimulus withthe one or more sensory thresholds, determining, based at least on themediation data, a mediation to be provided to the user, the mediationconfigured to provide the user relief from distractibility, inattention,anxiety, fatigue, or sensory issues; and providing the mediation to theuser, the mediation comprising an alert mediation, a guidance mediation,or a filter mediation.

In some implementations, the operations further comprise: storing afirst identifier that indicates whether the user is neurodiverse orneurotypical; and determining the one or more sensory thresholdsspecific to the user and the mediation data corresponding to one or moremediations specific to the user, comprises: determining, using at leastthe first identifier and the user sensory sensitivity data, the one ormore sensory thresholds and the mediation data.

In some implementations, the operations further comprise: receiving userdemographic data corresponding to user input indicating an age,education level, or gender of the user; and determining the one or moresensory thresholds specific to the user and the mediation datacorresponding to one or more mediations specific to the user, comprises:determining, using at least the first identifier, the user sensorysensitivity data, and the user demographic data, the one or more sensorythresholds and the mediation data.

In some implementations, the first identifier indicates whether or notthe user is autistic.

In some implementations, the first identifier indicates that the user isautistic.

In some implementations, the mediation is configured to provide the userrelief from fatigue; the mediation comprises the filter mediation; andthe filter mediation comprises filtering, in real-time, an audio signalpresented to the user or an optical signal presented to the user.

In some implementations, the mediation is configured to provide the userrelief from a distraction by increasing a response time of the user tothe distraction.

In some implementations, obtaining the user sensory sensitivity datacomprises receiving, at a graphical user interface, one or more firstresponses by the user to one or more first prompts indicating whetherthe user is visually sensitive, sonically sensitive, or interoceptivelysensitive; and the operations further comprise deriving the firstidentifier indicating that the user is autistic by: receiving, at thegraphical user interface, one or more second responses by the user toone or more second prompts indicating an anxiety level of the user;deriving, based on the sensory sensitivity data, one or more sensorysensitivity scores comprising a visual sensitivity score, a sonicsensitivity score, or an interoceptive sensitivity score; deriving,based on the one or more second responses, an anxiety score; andpredicting, using a model that predicts a probability of autism based atleast on an anxiety level and one or more sensory sensitivity levels,based at least on the anxiety score and the one or more sensorysensitivity scores, that the user is autistic.

In some implementations, obtaining the user sensory sensitivity datacomprises receiving, at a graphical user interface, one or more firstresponses by the user to one or more first prompts indicating whetherthe user is visually sensitive, sonically sensitive, or interoceptivelysensitive; and the operations further comprise deriving the firstidentifier indicating that the user is autistic by: receiving, at thegraphical user interface, one or more second responses by the user toone or more second prompts indicating a fatigue level of the user;deriving, based on the sensory sensitivity data, one or more sensorysensitivity scores comprising a visual sensitivity score, a sonicsensitivity score, or an interoceptive sensitivity score; deriving,based on the one or more second responses, a fatigue score; andpredicting, using a model that predicts a probability of autism based atleast on a fatigue level and one or more sensory sensitivity levels,based at least on the fatigue score and the one or more sensorysensitivity scores, that the user is autistic.

In some implementations, obtaining the user sensory sensitivity datafurther comprises: recording, using at least the one or more sensors, aresponse by the user to a visual stimulus, a sonic stimulus, or aphysiological stimulus.

In some implementations, the mediation comprises a combination mediationof at least two mediations selected from the alert mediation, theguidance mediation, and the filter mediation.

In some implementations, the combination mediation comprises the alertmediation followed by the filter mediation.

In some implementations, the alert mediation comprises alerting the userabout a distraction that is visual or auditory; and the filter mediationcomprises: comprising filtering, in real-time, an audio or opticalsignal presented to the user, the audio or optical signal associatedwith the distraction.

In some implementations, the system further comprises one or more fognodes configured to locally store sensor data collected by the one ormore sensors, the sensor data including first sensor data associatedwith the sensory input stimulus.

In some implementations, storing the one or more sensory thresholds andthe mediation data, comprises: locally storing, using the one or morefog nodes, the one or more sensory thresholds and the mediation data;and comparing the sensory input stimulus with the one or more sensorythresholds, comprises: comparing, using the one or more fog nodes, thesensory input stimulus with the one or more sensory thresholds.

In some implementations, the system further comprises one or more edgenodes configured to communicatively couple to the one or more fog nodesand a cloud server remotely located from the wearable device.

In some implementations, the one or more edge nodes are configured to:encrypt the first sensor data associated with the sensory input stimulusto obtain encrypted data; transmit the encrypted data to the cloudserver; and receive a response from the cloud server.

In some implementations, the one or more fog nodes and the one or moreedge nodes reside on a local area network (LAN) containing the wearabledevice, an ad-hoc network containing the wearable device, a LAN of amobile device directly coupled to the wearable device, or an ad-hocnetwork of the mobile device.

In some implementations, the sensor data comprises second sensor datathat does not trigger a mediation; and the system is configured suchthat the second sensor data that does not trigger a mediation is notmade available to any cloud server remotely located from the wearabledevice.

In some implementations, the mediation comprises the filter mediationthat comprises filtering, in real-time, an optical signal presented tothe user; the first sensor data associated with the sensory inputstimulus comprises first image data; the one or more edge nodes or theone or more fog nodes are configured to determine whether the firstimage data is sufficiently similar to second image data stored at thecloud server; and determining the mediation to be provided to the usercomprises in response to determining that the first image data issufficiently similar to the second image data, determining the filtermediation.

In some implementations, the mediation comprises the filter mediationthat comprises filtering, in real-time, an audio signal presented to theuser; the first sensor data associated with the sensory input stimuluscomprises first audio data; the one or more edge nodes or the one ormore fog nodes are configured to determine whether the first audio datais sufficiently similar to second audio data stored at the cloud server;and determining the mediation to be provided to the user comprises inresponse to determining that the first audio data is sufficientlysimilar to the second audio data, determining the filter mediation.

In some implementations, the operations further comprise: presenting tothe user, on a graphical user interface, one or more access controls forcontrolling user data that is made available to one or more other users,the user data comprising sensor data collected by the one or moresensors, the one or more sensory thresholds, the mediation data, or arecord of mediations presented to the user; and receiving datacorresponding to user input selecting the one or more access controls.For example, the one or more access controls may be configured such thatonly sensor data that triggered a mediation is accessible to one or moreother users (e.g., a general practitioner, a therapist, a family member,etc.). As another example, the one or more access controls can beconfigured such that certain types of sensor data (e.g., image or audiodata of the environment) are not made available to other users. As afurther example, the one or more access controls can be configured suchthat there are different hierarchies of data access, where some usershave more access to certain types of data than other users.

In some implementations, the operations further comprise: presenting tothe user, on a graphical user interface, one or more access controlsthat grant or deny access to one or more other users to influencemediations that are presented to the user; and receiving datacorresponding to user input actuating the one or more access controls.For example, a wearer user can grant a therapist user access to modifythe user's preferences to optimize the mediation that is presented tothe user. In some implementations, certain types of mediations can bedisabled or enabled.

In some implementations, the operations further comprise: presenting tothe user, on a graphical user interface, a graphical summary of progressof the user from using the wearable device, the graphical summaryincluding a moving average or change of time between mediations. Asanother example, the graphical summary of progress of the user canindicate a change in the sensory thresholds and/or mediations over time,a change/moving average of the user's average response time todistracting stimuli, a change/moving average of the number of mediationsrequired in some time frame (e.g., during the day) and/or some event(e.g., while in the workplace or classroom), etc.

In some implementations, the one or more sensors comprise multiplesensors of different types, the multiple sensors comprising: an auditorysensor, a galvanic skin sensor, a pupillary sensor, a body temperaturesensor, a head sway sensor, or an inertial movement unit; recording thesensory input stimulus to the user comprises obtaining first sensorydata corresponding to a first sensory input stimulus from a first sensorof the multiple sensors, and second sensory data corresponding to asecond sensory input stimulus from a second sensor of the multiplesensors; and determining the mediation to be provided to the user,comprises: inputting at least the first sensory data and the secondsensory data into a fusion-based deep learning (FBDL) model that outputsan identification of the mediation to be provided to the user.

In some implementations, determining the mediation to be provided to theuser, comprises: inputting at least the first sensory data, the secondsensory data, and the mediation data into the FBDL model that outputsthe identification of the mediation to be provided to the user.

One embodiment of the application is directed to a method, comprising:obtaining, at a wearable device system, user sensory sensitivity datacorresponding to user input indicating whether a user of a wearabledevice of the wearable device system is visually sensitive, sonicallysensitive, or interoceptively sensitive; determining, at the wearabledevice system, using at least the user sensory sensitivity data, one ormore sensory thresholds specific to the user and mediation datacorresponding to one or more mediations specific to the user, the one ormore sensory threshold selected from auditory, visual, or physiologicalsensory thresholds; storing, at a storage of the wearable device system,the one or more sensory thresholds and the mediation data; recording,using one or more sensors of the wearable device system, a sensory inputstimulus to the user; comparing, at the wearable device system, thesensory input stimulus with the one or more sensory thresholds specificto the user; in response to comparing the sensory input stimulus withthe one or more sensory thresholds, determining, based at least on themediation data, a mediation to be provided to the user, the mediationconfigured to provide the user relief from distractibility, inattention,anxiety, fatigue, or sensory issues; and providing, using at least thewearable device, the mediation to the user, the mediation comprising analert mediation, a guidance mediation, or a filter mediation.

One embodiment of the application is directed to a system, comprising: awearable device comprising one or more sensors; one or more processors;and one or more non-transitory computer-readable media having executableinstructions stored thereon that, when executed by the one or moreprocessors, cause the system to perform operations comprising:connecting to a datastore that stores one or more sensory thresholdsspecific to a user of the wearable device, the one or more sensorythresholds selected from auditory, visual or physiological sensorythresholds; recording, using the one or more sensors, a sensory inputstimulus to the user; comparing the sensory input stimulus with the oneor more sensory thresholds specific to the user to determine anintervention to be provided to the user, the intervention configured toprovide the user relief from distractibility, inattention, anxiety,fatigue, or sensory issues; and providing the intervention to the user,the intervention comprising filtering, in real-time, an audio signalpresented to the user or an optical signal presented to the user. Thephysiological sensory thresholds can bephysiological/psychophysiological sensory thresholds.

In some implementations, the operations further comprise:communicatively coupling the system to an Internet of Things (IoT)device, the sensory input stimulus generated at least in part due tosound emitted by a speaker of the IoT device or light emitted by a lightemitting device of the IoT device; and providing the intervention to theuser, comprises: controlling the IoT device to filter, in real-time, theaudio signal or the optical signal.

In some implementations, the IoT device comprises the light emittingdevice; controlling the IoT device to filter, in real-time, the audiosignal or the optical signal, comprises controlling the IoT device tofilter, in real-time, the optical signal; and filtering the opticalsignal adjusts a brightness or color of light output by the lightingdevice.

In some implementations, the IoT device comprises the speaker;controlling the IoT device to filter, in real-time, the audio signal orthe optical signal, comprises controlling the IoT device to filter, inreal-time, the audio signal; and filtering the audio signal adjusts afrequency of sound output by the speaker.

In some implementations, the wearable device further comprises a boneconduction transducer or a hearing device; and providing theintervention to the user comprises: filtering, at the wearable device,in real-time, the audio signal in a frequency domain; and afterfiltering the audio signal, presenting the audio signal to the user byoutputting, using the bone conduction transducer or the hearing device,a vibration or sound wave corresponding to the audio signal.

In some implementations, the wearable device further comprises a headmounted display (HMD) that presents the optical signal to the user, theHMD worn by the user; and providing the intervention to the user furthercomprises filtering, in real-time, the optical signal by modifying areal-time image of the real-world environment presented to the user viathe HMD.

In some implementations, comparing the sensory input stimulus with theone or more sensory thresholds specific to the user to determine theintervention to be provided to the user, comprises: determining, basedon the same sensor data recorded by the one or more sensors, to filterthe audio signal and to filter the optical signal.

In some implementations, the wearable device further comprises a HMDthat presents the optical signal to the user, the HMD worn by the user;and providing the intervention to the user includes filtering, inreal-time, the optical signal by modifying a real-time image of thereal-world environment presented to the user via the HMD.

In some implementations, modifying the real-time image comprisesinserting a virtual object into the real-time image or modifying theappearance of an object of the real-world environment in the real-timeimage.

In some implementations, comparing the sensory input stimulus with theone or more sensory thresholds specific to the user to determine theintervention to be provided to the user, comprises: inputting thesensory input stimulus and the one or more user-specific sensorythresholds into a trained model to automatically determine, based on anoutput of the trained model, a visual intervention to be provided to theuser.

In some implementations, the one or more sensors comprise multiplesensors of different types, the multiple sensors comprising: an auditorysensor, a galvanic skin sensor, a pupillary sensor, a body temperaturesensor, a head sway sensor, or an inertial movement unit; recording thesensory input stimulus to the user comprises recording a first sensoryinput stimulus from a first sensor of the multiple sensors, and a secondsensory input stimulus from a second sensor of the multiple sensors; andinputting the sensory input stimulus into the trained model comprisesinputting the first sensory input stimulus and the second sensory inputstimulus into the trained model.

In some implementations, the visual intervention comprises: presentingan alert to the user of a visually distracting object; and after it isdetermined that the user does not sufficiently respond to the alertwithin a period of time, filtering, in real-time, the optical signalpresented to the user.

In some implementations, the visual intervention comprises: filtering,in real-time, the optical signal to hide a visually distracting objectwithout providing a prior alert to the user that the visuallydistracting object is present.

In some implementations, the operations further comprise determining theone or more sensory thresholds specific to the user and one or moreinterventions specific to the user by: presenting multiple selectabletemplates to the user, each of the templates providing an indication ofwhether the user is visually sensitive, sonically sensitive, orinteroceptively sensitive, and each of the templates associated withcorresponding one or more sensory thresholds and one or moreinterventions; and receiving data corresponding to input by the userselecting one of the templates.

In some implementations, determining the one or more sensory thresholdsspecific to the user and the one or more interventions specific to theuser further comprises: receiving additional data corresponding toadditional user input selecting preferences, the preferences comprisingaudio preferences, visual preferences, physiological preferences, alertpreferences, guidance preferences, or intervention preferences; and inresponse to receiving the additional data, modifying the one or morethresholds and the one or more interventions of the selected template toderive the one or more sensory thresholds specific to the user and theone or more interventions specific to the user. In some implementations,the physiological preferences are psychophysiological preferences.

In some implementations, comparing the sensory input stimulus with theone or more sensory thresholds specific to the user to determine theintervention to be provided to the user, comprises: inputting thesensory input stimulus and the one or more user-specific sensorythresholds into a trained model to automatically determine, based on anoutput of the trained model, the intervention to be provided to theuser.

In some implementations, the user is neurodiverse. In someimplementations, the user can be autistic.

In some implementations, the intervention further comprises an alertintervention; and with the alert intervention, a response time for theuser increases by at least 3% and accuracy increases by at least about26% from baseline for errors of commission, the errors of commissionbeing a measure of a failure of the user to inhibit a response whenprompted by a feedback device.

In some implementations, the intervention further comprises a guidanceintervention; and with the guidance intervention, a response time forthe user increases by at least about 20% and accuracy increases by atleast about 10% from baseline for errors of commission, the errors ofcommission being a measure of a failure of the user to inhibit aresponse when prompted by a feedback device.

In some implementations, the intervention further comprises a guidanceintervention; and with the guidance intervention, a response time forthe user increases by at least about 2% and accuracy increases by atleast about 30% from baseline for errors of omission, the errors ofomission being a measure of a failure of the user to take appropriateaction when a prompt is not received from a feedback device.

In some implementations, with the intervention to filter, a responsetime for the user increases by at least about 10% from baseline forerrors of omission, the errors of omission being a measure of a failureof the user to take appropriate action when a prompt is not receivedfrom a feedback device.

In some implementations, with the intervention to filter, a responsetime for the user is at least about 15% faster than would be a responsetime for a neurotypical user using the system for errors of omission,the errors of omission being a measure of a failure of the user to takeappropriate action when a prompt is not received from a feedback device.

In some implementations, the intervention further comprises a guidanceintervention; and with the guidance intervention, a response time forthe user is at least about 20% faster and accuracy is about 8% higherthan would be a response time and accuracy of a neurotypical user usingthe system for errors of commission, the errors of commission being ameasure of a failure of the user to inhibit a response when prompted bya feedback device.

In some implementations, the intervention further comprises an alertintervention; and with the alert intervention, accuracy for the user isat least about 25% higher than would be an accuracy of a neurotypicaluser using the system for errors of commission, the errors of commissionbeing a measure of a failure of the user to inhibit a response whenprompted by a feedback device.

One embodiment of the application is directed to a method, comprising:connecting a wearable device system to a datastore that stores one ormore sensory thresholds specific to a user of a wearable device of thewearable device system, the one or more sensory thresholds selected fromauditory, visual or physiological sensory thresholds; recording, usingone or more sensors of the wearable device, a sensory input stimulus tothe user; comparing, using the wearable device system, the sensory inputstimulus with the one or more sensory thresholds specific to the user todetermine an intervention to be provided to the user, the interventionconfigured to provide the user relief from distractibility, inattention,anxiety, fatigue, or sensory issues; and providing, using the wearabledevice system, the intervention to the user, the intervention comprisingfiltering, in real-time, an audio signal presented to the user or anoptical signal presented to the user. In some implementations, thephysiological preferences are psychophysiological preferences.

In some implementations, the method further comprises communicativelycoupling the wearable device system to an IoT device; providing theintervention to the user comprises controlling the IoT device to filter,in real-time, the audio signal or the optical signal; and the sensoryinput stimulus generated at least in part due to sound emitted by aspeaker of the IoT device or light emitted by a light emitting device ofthe IoT device.

In some implementations, the IoT device comprises the light emittingdevice; controlling the IoT device to filter, in real-time, the audiosignal or the optical signal, comprises controlling the IoT device tofilter, in real-time, the optical signal; and filtering the opticalsignal adjusts a brightness or color of light output by the lightingdevice.

In some implementations, the IoT device comprises the speaker;controlling the IoT device to filter, in real-time, the audio signal orthe optical signal, comprises controlling the IoT device to filter, inreal-time, the audio signal; and filtering the audio signal adjusts afrequency of sound output by the speaker.

In some implementations, the wearable device further comprises a boneconduction transducer or a hearing device; and providing theintervention to the user comprises: filtering, at the wearable device,in real-time, the audio signal in a frequency domain; and afterfiltering the audio signal, presenting the audio signal to the user byoutputting, using the bone conduction transducer or the hearing device,a vibration or sound wave corresponding to the audio signal.

One embodiment of this application is directed to a system for providingsensory relief from distractibility, inattention, anxiety, fatigue,sensory issues, or combinations thereof, to a user in need thereof, thesystem comprising: (i.) a wearable device; (ii) a database of one ormore user-specific sensory thresholds selected from auditory, visual,and physiological sensory thresholds, one or more user-specific sensoryresolutions selected from auditory, visual and physiological sensoryresolutions, or combinations thereof; (iii) an activation means forconnecting the wearable device and the database; (iv) one or moresensors for recording a sensory input stimulus to the user; (v) acomparing means for comparing the sensory input stimulus recorded by theone or more sensors with the database of one or more user-specificsensory thresholds to obtain a sensory resolution for the user; (vi) oneor more feedback devices for transmitting the sensory resolution to theuser; and (vii) a user-specific intervention means for providing reliefto the user from the distractibility, inattention, anxiety, fatigue,sensory issues, or combinations thereof. The user-specific interventionmeans is selected from an alert intervention, a filter intervention, aguidance intervention, or a combination thereof, and the user can be aneurodiverse user or a neurotypical user. In a preferred embodiment, theneurodiverse user can be an autistic user. In some implementations, thephysiological sensory thresholds are psychophysiological sensorythresholds, and the physiological sensory resolutions arepsychophysiological sensory resolutions.

In some implementations, the wearable device is an eyeglass framecomprising the one or more sensors and the one or more feedback devices.

In some implementations, the one or more sensors are selected from oneor more infrared sensors, one or more auditory sensors, one or moregalvanic skin sensors, one or more inertial movement units, orcombinations thereof.

In some implementations, the one or more feedback devices are selectedfrom one or more haptic drivers, one or more bone conductiontransducers, or combinations thereof.

In some implementations, the system further comprises a wireless orwired hearing device.

In some implementations, the sensory input stimulus is selected from anecological auditory input, an ecological visual input, a egocentricphysiological/psychophysiological input, or combinations thereof.

In some implementations, the sensory input stimulus is measured byevaluating one or more parameters selected from eye tracking,pupillometry, auditory cues, interoceptive awareness, physical movement,variations in body temperature or ambient temperatures, pulse rate,respiration, or combinations thereof.

In some implementations, the sensory resolution is provided by one ormore alerts selected from a visual alert, an auditory alert, aphysiological/psychophysiological alert, a verbal alert, or combinationsthereof.

In some implementations, the activation means is a power switch locatedon the wearable device.

In some implementations, the power switch is located at a left side ofthe wearable device.

In some implementations, the power switch is located at a right side ofthe wearable device.

In some implementations, the power switch is a recessed power switch.

In some implementations, the database is stored in a storage device.

In some implementations, the storage device is selected from a fixed ormovable computer system, a portable wireless device, a smartphone, atablet, or combinations thereof.

In some implementations, with an alert intervention, a response time forautistic users increases by at least about 3% and accuracy increases byat least about 26% from baseline for errors of commission, wherein theerrors of commission are a measure of the user's failure to inhibit aresponse when prompted by the feedback device.

In some implementations, with an alert intervention, a response time forneurotypical users increases by at least about 18% and accuracyincreases by at least about 2.0% from baseline for errors of commission,wherein the errors of commission are a measure of the user's failure toinhibit a response when prompted by the feedback device.

In some implementations, with a guidance intervention, a response timefor autistic users increases by at least about 20% and accuracyincreases by at least about 10% from baseline for errors of commission,wherein the errors of commission are a measure of the user's failure toinhibit a response when prompted by the feedback device.

In some implementations, with guidance intervention, a response time forautistic users increases by at least about 2% and accuracy increases byat least about 30% from baseline for errors of omission, wherein theerrors of omission is a measure of the user's failure to takeappropriate action when a prompt is not received from the feedbackdevice.

In some implementations, with a filter intervention, a response time forautistic users increases by at least about 10% from baseline for errorsof omission, wherein the errors of omission is a measure of the user'sfailure to take appropriate action when a prompt is not received fromthe feedback device.

In some implementations, with a filter intervention, a response time forautistic users is at least about 15% faster than neurotypical users forerrors of omission, wherein the errors of omission are a measure of theuser's failure to take appropriate action when a prompt is not receivedfrom the feedback device.

In some implementations, with a guidance intervention, a response timefor autistic users is at least about 20% faster and accuracy is about 8%higher than neurotypical users for errors of commission, wherein theerrors of commission are a measure of the user's failure to inhibit aresponse when prompted by the feedback device.

In some implementations, with an alert intervention, accuracy forautistic users is at least about 25% higher than neurotypical users forerrors of commission, wherein the errors of commission are a measure ofthe user's failure to inhibit a response when prompted by the feedbackdevice.

In one embodiment, a method of providing sensory relief fromdistractibility, inattention, anxiety, fatigue, sensory issues, orcombinations thereof, to a user in need thereof, comprises: creating adatabase of one or more user-specific sensory thresholds selected fromauditory, visual and physiological/psychophysiological sensorythresholds, one or more user-specific sensory resolutions selected fromauditory, visual and physiological/psychophysiological sensoryresolution, or combinations thereof; attaching a wearable device to theuser, wherein the wearable device comprises one or more sensors and oneor more feedback devices; activating and connecting the wearable deviceto the database; recording a sensory input stimulus to the user via theone or more sensors; comparing the sensory input stimulus with thedatabase of one or more user-specific sensory thresholds; selecting anappropriate user-specific sensory resolution from the database;delivering the user-specific sensory resolution to the user via the oneor more feedback devices; and providing a user-specific intervention(a/k/a digital mediation) to provide relief to the user from thedistractibility, inattention, anxiety, fatigue, sensory issues, orcombinations thereof, wherein the user-specific intervention is selectedfrom an alert intervention, a filter intervention, a guidanceintervention, or a combination thereof, and wherein the user is anautistic user, a neurotypical user, or a neurodiverse user.

In some implementations, the one or more sensors are selected from oneor more infrared sensors, one or more microphones, one or more galvanicskin sensors, one or more inertial movement units, or combinationsthereof.

In some implementations, the one or more feedback devices is selectedfrom one or more haptic drivers, one or more bone conductiontransducers, or combinations thereof.

In some implementations, the sensory input stimulus is selected from anauditory input, a visual input, a physiological/psychophysiologicalinput or combinations thereof.

In some implementations, the sensory input stimulus is measured by oneor more parameters selected from eye tracking, pupillometry, auditorycues, interoceptive awareness, physical movement, variations in body orambient temperatures, pulse rate, respiration, or combinations thereof.

In some implementations, the user-specific sensory resolution isprovided by one or more alerts selected from a visual alert, an auditoryalert, a physiological/psychophysiological alert, a verbal alert orcombinations thereof.

In some implementations, the activation and connection of the wearabledevice to the database is through a power switch located on the wearabledevice.

In some implementations, the power switch is located at a left side ofthe wearable device or a right side of the wearable device

In some implementations, the power switch is a recessed power switch.

In some implementations, the wearable device is an eyeglass frame.

In some implementations, the database is stored in a storage device.

In some implementations, the storage device is selected from a fixed ormovable computer system, a portable wireless device, a smartphone, atablet, or combinations thereof.

In some implementations, with an alert intervention, a response time forautistic users increases by at least about 3% and accuracy increases byat least about 26% from baseline for errors of commission, wherein theerrors of commission are a measure of the user's failure to inhibit aresponse when prompted by the feedback device.

In some implementations, with an alert intervention, a response time forneurotypical users increases by at least about 18% and accuracyincreases by at least about 2.0% from baseline for errors of commission,wherein the errors of commission are a measure of the user's failure toinhibit a response when prompted by the feedback device.

In some implementations, with a guidance intervention, a response timefor autistic users increases by at least about 20% and accuracyincreases by at least about 10% from baseline for errors of commission,wherein the errors of commission are a measure of the user's failure toinhibit a response when prompted by the feedback device.

In some implementations, with a guidance intervention, a response timefor autistic users increases by at least about 2% and accuracy increasesby at least about 30% from baseline for errors of omission, wherein theerrors of omission are a measure of the user's failure to takeappropriate action when a prompt is not received from the feedbackdevice.

In some implementations, with a filter intervention, a response time forautistic users increases by at least about 10% from baseline for errorsof omission, wherein the errors of omission are a measure of the user'sfailure to take appropriate action when a prompt is not received fromthe feedback device.

In some implementations, with a filter intervention, a response time forautistic users is at least about 15% faster than neurotypical users forerrors of omission, wherein the errors of omission are a measure of theuser's failure to take appropriate action when a prompt is not receivedfrom the feedback device.

In some implementations, with a guidance intervention, a response timefor autistic users is at least about 20% faster and accuracy is about 8%higher than neurotypical users for errors of commission, wherein theerrors of commission are a measure of the user's failure to inhibit aresponse when prompted by the feedback device.

In some implementations, with an alert intervention, accuracy forautistic users is at least about 25% higher than neurotypical users forerrors of commission, wherein the errors of commission are a measure ofthe user's failure to inhibit a response when prompted by the feedbackdevice.

In one embodiment, a wearable device comprises one or more sensors andone or more feedback devices, wherein a combination of the one or moresensors and the one or more feedback devices provides sensory relieffrom distractibility, inattention, anxiety, fatigue, sensory issues, orcombinations thereof, to a user/wearer in need thereof.

In some implementations, the wearable device is an eyeglass frame.

In some implementations, the one or more sensors are connected to theeyeglass frame.

In some implementations, the one or more feedback devices are connectedto the eyeglass frame.

In some implementations, the eyeglass frame comprises a rim, twoearpieces and hinges connecting the earpieces to the rim.

In some implementations, the one or more sensors are selected from thegroup consisting of one or more infrared sensors, one or more auditorytransducers, one or more galvanic skin sensors, one or more inertialmovement units, or combinations thereof.

In some implementations, the infrared sensor is surface-mounted on aninner side of the wearable device.

In some implementations, the infrared sensor is arranged to be incidenton a right eye, a left eye or both eyes of a user.

In some implementations, the auditory transducer is a subminiaturemicrophone.

In some implementations, the subminiature microphone is surface-mountedon an outer side of the wearable device.

In some implementations, the wearable device comprises at least twoauditory transducers, wherein a first auditory transducer is arranged atan angle of about 1100 to a second auditory transducer.

In some implementations, the galvanic skin sensor is surface-mounted onan inner side of the wearable device, and wherein the galvanic skinsensor is in direct contact with skin of a user.

In some implementations, the inertial movement unit isinternally-mounted on an inner-side of the wearable device.

In some implementations, the one or more feedback devices are selectedfrom one or more haptic drivers, one or more bone conductiontransducers, or combinations thereof.

In some implementations, the haptic drive is internally mounted on aninner side of the wearable device.

In some implementations, the haptic drive is internally mounted on aninner side of the wearable device and behind the inertial movement unit.

In some implementations, the haptic drive provides a vibration patternin response to a sensory input stimulus selected from eye tracking,pupillometry, auditory cues, interoceptive awareness, physical movement,variations in body or ambient temperature, pulse rate, respiration, orcombinations thereof.

In some implementations, the stereophonic bone conduction transducer issurface-mounted on an inner side of the wearable device, and thestereophonic bone conduction transducer is in direct contact with auser's skull.

In some implementations, the stereophonic bone conduction transducerprovides an auditory tone, a pre-recorded auditory guidance, real-timefiltering, or combinations thereof, in response to a sensory inputstimulus selected from eye tracking, pupillometry, auditory cues,interoceptive awareness, physical movement, variations in body orambient temperature, pulse rate, respiration, or combinations thereof.

In some implementations, the wearable device further comprises anoptional wireless or wired hearing device.

In some implementations, the wearable device further comprises anintervention means to providing relief to a user from thedistractibility, inattention, anxiety, fatigue, sensory issues, orcombinations thereof, the intervention means selected from an alertintervention, a filter intervention, a guidance intervention, or acombination thereof.

In some implementations, the wearable device further comprises a powerswitch. The power switch can be located at a left side of the wearabledevice or a right side of the wearable device. The power switch can be arecessed power switch.

In one embodiment, a non-transitory computer-readable medium hasexecutable instructions stored thereon that, when executed by aprocessor, cause a wearable device to perform operations comprising:connecting the wearable device to a datastore that stores one or moresensory thresholds and one or more sensory resolutions specific to auser, the one or more sensory thresholds selected from auditory, visualor physiological/psychophysiological sensory thresholds, and the one ormore sensory resolutions selected from auditory, visual, orphysiological/psychophysiological sensory resolutions; recording, viaone or more sensors, a sensory input stimulus to the user; comparing thesensory input stimulus recorded by the one or more sensors with one ormore sensory thresholds to obtain a sensory resolution for the user; andtransmitting the sensory resolution to the user.

In some implementations, the operations further comprise:communicatively coupling to an IoT device providing the sensory inputstimulus to the user; and transmitting the sensory resolution to theuser, comprises: after communicatively coupling to the IoT device,controlling the IoT device to transmit the sensory resolution.

In some implementations, the IoT device comprises a networked lightingdevice; and controlling the IoT device to transmit the sensoryresolution, comprises: controlling a brightness or color output of thenetworked lighting device.

In some implementations, the IoT device comprises a networked speaker;and controlling the IoT device to transmit the sensory resolution,comprises: controlling a volume, an equalization setting, or a channelbalance of the networked speaker.

In some implementations, comparing the sensory input stimulus recordedby the one or more sensors with the one or more user-specific sensorythresholds to obtain the sensory resolution for the user, comprises:inputting the sensory input stimulus and the one or more user-specificsensory thresholds into a trained model to automatically determine thesensory resolution for the user.

In some implementations, the operations further comprise determining theone or more sensory thresholds and the one or more sensory resolutionsby: presenting multiple selectable templates to the user, each of thetemplates providing an indication of whether the user is visuallysensitive, sonically sensitive, or interoceptively sensitive, and eachof the templates associated with corresponding one or more thresholdsand one or more sensory resolutions; and receiving data corresponding toinput by the user selecting one of the templates.

In some implementations, determining the one or more user-specificsensory thresholds and the one or more user-specific sensory resolutionsfurther comprises: receiving additional data corresponding to additionaluser input selecting preferences, the preferences comprising audiopreferences, visual preferences, physiological/psychophysiologicalpreferences, alert preferences, guidance preferences, or interventionpreferences; and in response to receiving the additional data, modifyingthe one or more thresholds and one or more sensory resolutions of theselected template to derive the one or more user-specific sensorythresholds and the one or more user-specific sensory resolutions.

Other features and aspects of the disclosed method will become apparentfrom the following detailed description, taken in conjunction with theaccompanying drawings, which illustrate, by way of example, the featuresin accordance with embodiments of the disclosure. The summary is notintended to limit the scope of the claimed disclosure, which is definedsolely by the claims attached hereto.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure, in accordance with one or more variousembodiments, is described in detail with reference to the followingfigures. The figures are provided for purposes of illustration only andmerely depict typical or example embodiments of the disclosure.

FIG. 1 is a schematic representation of a wearable device, in accordancewith some implementations of the disclosure.

FIG. 2 is a graphical representation of sensitivities across threemodalities—visual, aural and anxiety—as observed in Pre-Trial BatteryExamination (PTBE), as described herein.

FIG. 3 is a graphical representation of interest in a wearable deviceamong autism spectrum condition (ASC) participants in PTBE.

FIG. 4 is a flowchart of a standard study protocol of SustainedAttention to Response Task (SART) testing.

FIG. 5 is a flowchart of a standard Wizard of Oz (Wizard of Oz) studyprotocol.

FIG. 6 is a flowchart of the SART/WoZ study protocol, in accordance withsome implementations of the disclosure.

FIG. 7 is a graphical representation of recruitment scores of studyparticipants for the wearable device studies.

FIGS. 8A to 8C are graphical representations of the Errors of Commission(EOC) of the full cohort of participants in the SART/WoZ study describedherein. FIG. 8A shows the EOC from baseline to baseline. FIG. 8B showsthe EOC intervention effect. FIG. 8C shows the lasting effect of EOC.

FIGS. 9A to 9C are graphical representations of EOC as it relates toResponse Time (RT) of the full cohort of participants in the SART/WoZstudy described herein. FIG. 9A shows the EOC vs RT from startingbaseline to final baseline. FIG. 9B shows the EOC vs RT interventioneffect. FIG. 9C shows the lasting effect of EOC vs RT.

FIGS. 10A to 10C are graphical representations of EOC grouped by studyparticipants.

FIGS. 11A to 11C are graphical representations of EOC vs RT grouped bystudy participants.

FIGS. 12A to 12C are graphical representations of the Errors of Omission(EOO) of the full cohort of participants in the SART/WoZ study describedherein. FIG. 12A shows the EOO from starting baseline to final baseline.FIG. 12B shows the EOO intervention effect. FIG. 12C shows the lastingeffect of EOO.

FIGS. 13A to 13C are graphical representations of EOO as it relates toRT of the full cohort of participants in the SART/WoZ study describedherein. FIG. 13A shows the EOO vs RT from starting baseline to finalbaseline. FIG. 13B shows the EOO vs RT intervention effect. FIG. 13Cshows the lasting effect of EOO vs RT.

FIGS. 14A to 14C are graphical representations of EOO grouped by studyparticipants.

FIGS. 15A to 15C are graphical representations of EOO vs RT grouped bystudy participants.

FIG. 16 is a block diagram of components of a wearable device, inaccordance with some implementations of the disclosure.

FIG. 17 is a block diagram of additional microprocessor details (ARMprocessor) of a wearable device, in accordance with some implementationsof the disclosure.

FIG. 18 is a flowchart of the various components of the study variables,in accordance with some implementations of the disclosure.

FIG. 19 depicts a wearable device system including a wearable device incommunication with a mobile device and a datastore, in accordance withsome implementations of the disclosure.

FIG. 20 shows an operational flow diagram depicting an example methodfor initializing and iteratively updating one or more sensory thresholdsand one or more interventions associated with a specific user, inaccordance with some implementations of the disclosure.

FIG. 21 depicts a wearable device system including a wearable device incommunication with a mobile device that controls an IoT device with aspeaker, in accordance with some implementations of the disclosure.

FIG. 22 depicts a wearable device system including a wearable device incommunication with a mobile device that controls an IoT device with alight emitting device, in accordance with some implementations of thedisclosure.

FIG. 23 depicts an example wearable device that can be utilized toprovide visual interventions, in accordance with some implementations ofthe disclosure.

FIG. 24A depicts interventions that can be delivered using a real-timeoptical enhancement algorithm, the interventions including hapticalerts, tone alerts guidance, and an eraser effect, in accordance withsome implementations of the disclosure.

FIG. 24B depicts interventions that can be delivered using a real-timeoptical enhancement algorithm, the interventions including a text alert,a blur effect, and a cover-up effect, in accordance with someimplementations of the disclosure.

FIG. 24C depicts interventions that can be delivered using a real-timeoptical enhancement algorithm, the interventions including colorbalance, a contrast effect, and an enhancement effect, in accordancewith some implementations of the disclosure.

FIG. 25 depicts one particular example of a workflow that uses areal-time optical enhancement algorithm to provide interventions, inreal-time, in a scenario where there is a distracting visual source, inaccordance with some implementations of the disclosure.

FIG. 26 depicts a sensitivity mental health distractibility model, inaccordance with some implementations of the disclosure.

FIG. 27 is a flowchart depicting a design/method of the PPI studydescribed herein.

FIG. 28 depicts a word cloud derived from alternative, autistic-voicedexpressions during the PPI study described herein.

FIG. 29 depicts the mean distribution of anxiety and distractibilityscores for diagnostic groups across demographic variable for the PPIstudy described herein.

FIG. 30 depicts non-autistic mediation models, in accordance with someimplementations of the disclosure.

FIG. 31 depicts autistic mediation models, in accordance with someimplementations of the disclosure.

FIG. 32 depicts an autistic mediation model predicting distractibilityfrom auditory via fatigue, in accordance with some implementations ofthe disclosure.

FIG. 33 depicts an autistic mediation model predicting distractibilityfrom physiology via fatigue, in accordance with some implementations ofthe disclosure.

FIG. 34A shows summary results of the PPI study described herein.

FIG. 34B shows summary results of the PPI study described herein.

FIG. 35A shows summary results of the SART/WOz clinical study describedherein.

FIG. 35B shows summary results of the SART/WOz clinical study describedherein.

FIG. 36A is an operational flow diagram illustrating an example methodfor initializing and iteratively updating one or more sensory thresholdsand one or more mediations associated with a specific user, inaccordance with some implementations of the disclosure.

FIG. 36B is an operational flow diagram illustrating an example methodfor predicting whether a user is neurodiverse (e.g., autistic) orneurotypical, in accordance with some implementations of the disclosure.

FIG. 36C is an operational flow diagram illustrating an example methodfor predicting whether a user is neurodiverse (e.g., autistic) orneurotypical, in accordance with some implementations of the disclosure.

FIG. 37 illustrates an example system architecture/topology forimplementing fog data processing, in accordance with someimplementations of the disclosure.

FIG. 38A depicts a particular example of a wearable system architecture,including data flows, that leverages fog and edge computing, inaccordance with some implementations of the disclosure.

FIG. 38B is a flow diagram illustrating operations that are performed bythe system of FIG. 38A, in accordance with some implementations of thedisclosure.

FIG. 39 is a high-level flowchart of an Open Learner Model (OLM)framework, in accordance with some implementations of the disclosure.

FIG. 40 depicts a table of the OLM described herein, the tabledescribing what is available.

FIG. 41 depicts a flowchart of the OLM described herein, the flowchartdepicting what is available.

FIG. 42 depicts a table of the OLM described herein, the tabledescribing how the model is presented to stakeholders.

FIG. 43 depicts a flowchart of the OLM described herein, the flowchartdepicting how the model is presented to stakeholders.

FIG. 44 depicts a table of the OLM described herein, the tabledescribing who controls access over others.

FIG. 45 depicts a flowchart of the OLM described herein, the flowchartdepicting who controls access over others.

FIG. 46 depicts a system that implements an augmented reality-basedmultimodal learning analytic framework, in accordance with someimplementations of the disclosure.

FIG. 47 illustrates one example of a fusion-based, deep learning model,in accordance with some implementations of the disclosure.

FIG. 48 is a high level flow diagram conceptually illustrating theoperation of a multi-sensory assistive wearable system, in accordancewith some implementations of the disclosure.

The figures are not exhaustive and do not limit the disclosure to theprecise form disclosed.

DETAILED DESCRIPTION

Further aspects, features and advantages of this invention will becomeapparent from the detailed description which follows.

As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. Additionally, the use of “or” is intended to include“and/or”, unless the context clearly indicates otherwise.

As used herein, “about” is a term of approximation and is intended toinclude minor variations in the literally stated amounts, as would beunderstood by those skilled in the art. Such variations include, forexample, standard deviations associated with conventional measurementtechniques or specific measurement techniques described herein. All ofthe values characterized by the above-described modifier “about,” arealso intended to include the exact numerical values disclosed herein.Moreover, all ranges include the upper and lower limits.

Any apparatus, device or product described herein is intended toencompass apparatus, device or products which consist of, consistessentially of, as well as comprise, the various constituents/componentsidentified herein, unless explicitly indicated to the contrary.

As used herein, the recitation of a numerical range for a variable isintended to convey that the variable can be equal to any value(s) withinthat range, as well as any and all sub-ranges encompassed by the broaderrange. Thus, the variable can be equal to any integer value or valueswithin the numerical range, including the end-points of the range. As anexample, a variable which is described as having values between 0 and10, can be 0, 4, 2-6, 2.75, 3.19-4.47, etc.

In the specification and claims, the singular forms include pluralreferents unless the context clearly dictates otherwise. As used herein,unless specifically indicated otherwise, the word “or” is used in the“inclusive” sense of “and/or” and not the “exclusive” sense of“either/or.”

Unless indicated otherwise, each of the individual features orembodiments of the present specification are combinable with any otherindividual features or embodiments that are described herein, withoutlimitation. Such combinations are specifically contemplated as beingwithin the scope of the present invention, regardless of whether theyare explicitly described as a combination herein.

Technical and scientific terms used herein have the meaning commonlyunderstood by one of skill in the art to which the present descriptionpertains, unless otherwise defined. Reference is made herein to variousmethodologies and materials known to those of skill in the art.

As used herein, the term “alert intervention” can include: in the eventof an ecological and/or physiological (e.g., psychophysiological)threshold's activation that corresponds to a wearer's preferences, asignal is delivered to: (i) a haptic driver that provides a gentle,tactile vibration pattern to convey information to the wearer thatfocus, anxiety, fatigue or related characteristics require theirattention; and/or (ii) a bone conduction transducer that delivers anauditory/sonic message (e.g., pre-recorded text-to-speech, beep tone,etc.) reinforcing the haptic with an aural intervention and set ofinstructions.

As used herein, the term “filter intervention” can include: in the eventof an ecological and/or physiological (e.g., psychophysiological)threshold's activation that corresponds to a wearer's preferences andrequires auditory or optical filtering, performing audio signalprocessing or optical signal processing. Digital audio signal processingcan deliver real-time and low-latency audio signals that includecorrected amplitude (compression, expansion), frequency (dynamic,shelving, low/hi-cut, and parametric equalization), spatial realignment(reposition, stereo to mono) and/or phase correction (time delay, combfiltering, linear phase alignment). In an embodiment, the filterinvention can be delivered to a bone conduction transducer. In otherembodiments, the filter invention can be delivered to optional wirelessor wired hearing devices, including but not limited to earbuds,earphones, headphones, and the like.

As used herein, the term “guidance intervention” can include anintervention similar to an alert intervention, where the guidance can beprovided by way of step-by-step instructions for re-alignment of focus,head sway, pupillary activity, pulse, temperature, respiration, anxiety,and fatigue coaching. These pre-recorded, text-to-speech audio streamscan be delivered to bone conduction systems, which provide step-by-stepinstructional intervention both privately and unobtrusively.

As used herein, the term “combination intervention” can include asfollows: an intervention that can be selected by the wearer, which canbe a combination of alert, filter and guidance interventions, and whichare provided depending upon the triggering mechanism. For example, onlysonic disturbances can be addressed through filter intervention, whileall other issues (attentional-focus, anxiety, fatigue, and the like) canbe intervened through haptic, text-to-speech alerts, long-formstep-by-step guidance, and the like.

As used herein, the terms “user” and “wearer” are used interchangeably.

As used herein, the terms “intervention” and “mediation” are usedinterchangeably.

As used herein, the term “errors of commission” are a measure of theuser's failure to inhibit a response when prompted by the feedbackdevice

As used herein, the term “errors of omission” are a measure of theuser's failure to take appropriate action when a prompt is not receivedfrom the feedback device

As used herein, the term “response time” is intended to include the timetaken by a participant to respond to a sensory cue and/or an alert,filter and/or guidance intervention. Response Time may also beinterchangeably referred to as Reaction Time and is defined as theamount of time between when a participant perceives a sensory cue andwhen the participant responds to said sensory cue. Response Time orReaction Time is the ability to detect, process, and respond to astimulus.

As used herein to refer to processing such as, for example, anyprocessing that can include filtering of an audio signal and/or anoptical signal that is presented to a user, the term “real-time” isintended to refer to processing and/or filtering the signal with aminimal latency after the original audio signal and/or optical signaloccurs. For example, the latency can be a non-zero value of about 500milliseconds (ms) or less, about 250 ms or less, about 200 ms or less,about 150 ms or less, about 100 ms or less, about 90 ms or less, about80 ms or less, about 70 ms or less, about 60 ms or less, about 50 ms orless, about 40 ms or less, about 30 ms or less, about 20 ms or less, orabout 10 ms or less, ranges and/or combinations thereof and the like.The minimum latency can be subject to system and hardware and softwarelimitations, including communication protocol latency, digital signalprocessing latency, electrical signal processing latency, combinationsthereof and the like. In some instances, real-time filtering of an audiosignal and/or an optical signal can be perceived by a user as beingimmediate, instantaneous or nearly immediate and/or instantaneous.

Autism Spectrum Condition (ASC) is a life-long diagnosis, which has asubset of features including hyper-, seeking- and/or hypo-reactivity tosensory inputs or unusual interests. These qualities are evident acrossenvironmental (e.g., response to specific sounds, visual fascinationwith lights or movements) and physiological/psychophysiological domains(e.g., anxiety, respiration or euthermia). Scholars report that ninety(90%) of autistic adults experience sensory issues causing significantbarriers at school/work (Leekam et al., 2007). Individuals with ASCoften exhibit persistent deficits in social communication andinteraction across multiple contexts. An additional hallmark includesrestricted, repetitive patterns of behavior and interests (RRBI).Importantly, RRBIs include hyper-, seeking- and/or hypo-reactivity tosensory input along with attainably unusual interests in sensory aspectsof the environment and physiological/psychophysiological responses tovisuals, textures, smells, touch, and sounds. As the diagnosis of ASCpopulations increases exponentially over time, an ever-expanding socialpolicy chasm proliferates, whereby an autistic individual's smoothtransition into the fabric of daily life is often compromised. Expertsidentify this as a gap stemming from either: (i) stuntedpublic/government support for neurodiverse individuals; ii) tensionsbetween the autism community and society; and iii) limited support forlater-life educational/vocational pathways. The negative effects ofpolicy-related factors are a consequence resulting in societal coststhat have a potential to become still more significant and possiblyirremediable.

This application provides various interventions to alter, redirectand/or attenuate disruptive stimuli. Namely, described herein aresystems, devices and methods to determine whether distractions exist,which can be exacerbated at school and at work, and provideinterventions to compensate for such distractions, thereby lesseninganxiety for neurotypical and neurodiverse individuals, and providingsensory relief. This application aspires to help individuals learn,adapt, and internalize how best to respond to encroaching ecologicalstimuli and resulting physiological/psychophysiological responses.Wearables, as described herein, may, through repetitive processesobserved and experienced by users, pave the way for a call and responseprocess that may eventually transfer directly from a machine or systemto the person, thus embedding guidance for similarly reoccurring/futurescenarios. An autistic individual, for example, might watch, experience,and learn precisely how an Artificial Intelligence/Cognitive Enhancementsystem detects, filters and coaches herself when confronted with anundesirable sensory stimulus.

One embodiment is directed to a system for providing sensory relief fromdistractibility, inattention, anxiety, fatigue, sensory issues, orcombinations thereof, to a user in need thereof, the system comprising:(i.) a wearable device; (ii) a database of one or more user-specificsensory thresholds selected from auditory, visual andphysiological/psychophysiological sensory thresholds, one or moreuser-specific sensory resolutions selected from auditory, visual andphysiological/psychophysiological sensory resolutions, or combinationsthereof; (iii) an activation means for connecting the wearable deviceand the database; (iv) one or more sensors for recording a sensory inputstimulus to the user; (v) a comparing means for comparing the sensoryinput stimulus recorded by the one or more sensors with the database ofone or more user-specific sensory thresholds to obtain a sensoryresolution for the user; (vi) one or more feedback devices fortransmitting the sensory resolution to the user; and (vii) auser-specific intervention means for providing relief to the user fromthe distractibility, inattention, anxiety, fatigue, sensory issues, orcombinations thereof. The user-specific intervention means can beselected from an alert intervention, a filter intervention, a guidanceintervention, or a combination thereof. The user can be an autisticuser, a neurodiverse user or a neurotypical user.

Another embodiment is directed to a method of providing sensory relieffrom distractibility, inattention, anxiety, fatigue, sensory issues, orcombinations thereof, to a user in need thereof, the method comprising:(i) creating a database of one or more user-specific sensory thresholdsselected from auditory, visual and physiological/psychophysiologicalsensory thresholds, one or more user-specific sensory resolutionsselected from auditory, visual and physiological/psychophysiologicalsensory resolution, or combinations thereof; (ii) attaching a wearabledevice to the user, wherein the wearable device comprises one or moresensors and one or more feedback devices; (iii) activating andconnecting the wearable device to the database; (iv) recording a sensoryinput stimulus to the user via the one or more sensors; (v) comparingthe sensory input stimulus with the database of one or moreuser-specific sensory thresholds; (vi) selecting an appropriateuser-specific sensory resolution from the database; (vi) delivering theuser-specific sensory resolution to the user via the one or morefeedback devices; and (vii) providing a user-specific intervention toprovide relief to the user from the distractibility, inattention,anxiety, fatigue, sensory issues, or combinations thereof.

Another embodiment is directed to a wearable device comprising one ormore sensors and one or more feedback devices. According to furtherembodiments, the wearable device can be an eyeglass frame. One or moresensors and/or one or more feedback devices can be connected to theeyeglass frame. The eyeglass frame may comprise a rim, two earpieces andhinges connecting the earpieces to the rim. In alternate embodiments,the wearable device may include jewelry, smart clothing, andaccessories, including but not limited to rings, sensor woven fabrics,wristbands, watches, pins, hearing aid, assistive devices, medicaldevices, virtual, augmented, and mixed reality (VR/AR/MR) headsets, andthe like. The wearable device may have the ability to coordinate withmobile and/or network devices for alert, filter, and guidanceinterventions, and may include sensors and feedback devices in variouscombinations.

According to further embodiments, the one or more sensors can beselected from one or more infrared sensors, one or more auditorysensors, one or more galvanic skin sensors, one or more inertialmovement units, or combinations thereof. The infrared sensor can besurface-mounted on an inner side of the wearable device. The infraredsensor can be arranged to be incident on a right eye, a left eye or botheyes of a user.

According to further embodiments, the one or more feedback devices canbe selected from one or more haptic drivers, one or more bone conductiontransducers, or combinations thereof.

According to further embodiments, the wearable device may furthercomprise a wireless or wired hearing device.

According to further embodiments, the sensory input stimulus can beselected from an auditory input, a visual input, aphysiological/psychophysiological input or combinations thereof. Thesensory input stimulus can be measured by evaluating one or moreparameters selected from eye tracking, pupillometry, auditory cues,interoceptive awareness, physical movement, variations in body orambient temperature, pulse rate, respiration, or combinations thereof.The sensory resolution can be provided by one or more alerts selectedfrom a visual alert, an auditory alert, aphysiological/psychophysiological alert, a verbal alert or combinationsthereof.

According to further embodiments, the activation means can be a powerswitch located on the wearable device. The power switch can be locatedat a left side of the wearable device and/or at a right side of thewearable device. The power switch can be a recessed power switch. Inanother embodiment, power may be supplied when in stand-by mode from auser interface component, including but not limited to mobile phones,laptops, tablets, desktop computers, and the like, and any userinterface known in the field can be used without limitation. In anotherembodiment, the activation means may include a power switch or powersource that can be activated remotely (i.e., when not in proximity of auser).

In another embodiment, the activation means may be triggered by thewearable's accelerometer, pupillary and head sway sensors, and the like.For example, when an accelerometer is selected as an activation means,the accelerometer senses when the wearer (and wearable) is idle. In thisinstance, the unit can be in a low-power or power-off mode, and when thewearable is engaged (e.g., the wearable is lifted from a surface, moveor agitated), such engagement is recognized by the accelerometer, whichswitches the wearable into a power-on mode. Similarly, for example, whenpupillary or head sway sensors are used as an activation means, thepower management system includes the ability to place the unit into abattery conservation mode (e.g., low-power mode). If, for example, awearer was to shut their eyes whilst resting with a wearable “in place”,the sensors would react to a novel movement and immediately return thesystem into a powered-on state when/if the user was to eventually arisefrom a period of rest, and the like.

In another embodiment, an activation means may include a power-onactivity programmed from a biopotential analogue front end (AFE), whichincludes galvanic skin sensor response applications includingperspiration, heart rate, blood pressure, temperature, and the like, allof which can trigger an activation of the wearable device.

In another embodiment, the wearable device's activation can be fullyaccessed by any type of network device/protocol because of its IoTconnectivity, which enables communication, activation, and the like, ofthe wearable device.

According to further embodiments, a database can be stored in a storagedevice. The storage device can be selected from a fixed or movablecomputer system, a portable wireless device, a smartphone, a tablet, orcombinations thereof. In an alternate embodiment, the database can bestored locally on or in the wearable device. In another alternateembodiment, the databased can be stored remotely, including but notlimited to cloud-based systems, secured datacenters behind DMZ, and thelike, and the database can be in encrypted and decrypted communicationwith the secured wearable device and its data.

Based on any of the exemplary embodiments described herein, a responsetime for autistic users increases by at least about 0.5% to about 5%,about 1% to about 4.5%, about 1.5% to about 4%, about 2% to about 3.5%,and preferably about 3% after alert intervention and accuracy increasesby at least about 10% to about 50%, about 15% to about 40%, about 20% toabout 30%, and preferably about 26% from baseline for errors ofcommission, wherein the errors of commission are a measure of the user'sfailure to inhibit a response when prompted by the feedback device. Anumerical value within these ranges can be equal to any integer value orvalues within any of these ranges, including the end-points of theseranges.

Based on the exemplary embodiments described herein, a response time forneurotypical users increases by at least about 0.5% to about 50%, about5% to about 40%, about 10% to about 30%, about 15% to about 20%, andpreferably about 18% after alert intervention and accuracy increases byat least about 0.01% to about 5%, about 0.05% to about 4%, about 1% toabout 3%, and preferably about 2.0% from baseline for errors ofcommission, wherein the errors of commission are a measure of the user'sfailure to inhibit a response when prompted by the feedback device. Anumerical value within these ranges can be equal to any integer value orvalues within any of these ranges, including the end-points of theseranges.

Based on the exemplary embodiments described herein, a response time forautistic users increases by at least about 0.5% to about 50%, about 1%to about 40%, about 10% to about 30%, and preferably about 20% afterguidance intervention and accuracy increases by at least about 0.5% toabout 30%, about 1.0% to about 20%, about 5% to about 15%, andpreferably about 10% from baseline for errors of commission, wherein theerrors of commission are a measure of the user's failure to inhibit aresponse when prompted by the feedback device. A numerical value withinthese ranges can be equal to any integer value or values within any ofthese ranges, including the end-points of these ranges.

Based on the exemplary embodiments described herein, a response time forautistic users increases by at least about 0.01% to about 5%, about0.05% to about 4%, about 1% to about 3%, and preferably about 2% afterguidance intervention and accuracy increases by at least about 10% toabout 50%, about 15% to about 45%, about 20% to about 40%, andpreferably about 30% from baseline for errors of omission, wherein theerrors of omission is a measure of the user's failure to takeappropriate action when a prompt is not received from the feedbackdevice. A numerical value within these ranges can be equal to anyinteger value or values within any of these ranges, including theend-points of these ranges.

Based on the exemplary embodiments described herein, a response time forautistic users increases by at least about 0.5% to about 30%, about 1.0%to about 20%, about 5% to about 15%, and preferably about 10% frombaseline after filter intervention for errors of omission, wherein theerrors of omission is a measure of the user's failure to takeappropriate action when a prompt is not received from the feedbackdevice. A numerical value within these ranges can be equal to anyinteger value or values within any of these ranges, including theend-points of these ranges.

Based on the exemplary embodiments described herein, a response time forautistic users is at least about 0.5% to about 30%, about 1.0% to about25%, about 5% to about 20%, and preferably about 15% faster thanneurotypical users after filter intervention for errors of omission,wherein the errors of omission are a measure of the user's failure totake appropriate action when a prompt is not received from the feedbackdevice. A numerical value within these ranges can be equal to anyinteger value or values within any of these ranges, including theend-points of these ranges.

Based on the exemplary embodiments described herein, a response time forautistic users is at least about 0.5% to about 50%, about 1% to about40%, about 10% to about 30%, and preferably about 20% faster afterguidance intervention and accuracy is at least about 0.5% to about 30%,about 1.0% to about 20%, about 5% to about 15%, and preferably about 8%higher than neurotypical users for errors of commission, wherein theerrors of commission are a measure of the user's failure to inhibit aresponse when prompted by the feedback device. A numerical value withinthese ranges can be equal to any integer value or values within any ofthese ranges, including the end-points of these ranges.

Based on the exemplary embodiments described herein, accuracy forautistic users is at least about 0.5% to about 50%, about 1% to about4%, about 10% to about 30%, and preferably about 25% higher thanneurotypical users after alert intervention for errors of commission,wherein the errors of commission are a measure of the user's failure toinhibit a response when prompted by the feedback device. A numericalvalue within these ranges can be equal to any integer value or valueswithin any of these ranges, including the end-points of these ranges.

According to further embodiments, an auditory transducer can be asubminiature microphone. The subminiature microphone may preferably besurface-mounted on an outer side of the wearable device.

According to further embodiments, a wearable device may include at leasttwo auditory transducers, and the arrangement of the first and secondauditory transducers can be one that is known in the art, including butnot limited to the first and second auditory transducers being arrangedat an angle ranging from about 45° to about 135°, about 55° to about130°, about 65° to about 125°, about 75° to about 120°, about 85° toabout 120°, about 95° to about 115°, about 100°, about 110°, and thelike. The numerical value of any specific angle within these ranges canbe equal to any integer value or values within any of these ranges,including the end-points of these ranges can be.

According to further embodiments, a galvanic skin sensor can besurface-mounted on an inner side of the wearable device, and thegalvanic skin sensor can be in direct contact with skin of a user. Theinner side of the wearable device can be a side facing the skin orsubstantially facing the skin.

According to further embodiments, an inertial movement unit maypreferably be internally-mounted on an inner-side of the wearabledevice.

According to further embodiments, the one or more feedback devices canbe selected from one or more haptic drivers, one or more bone conductiontransducers, or combinations thereof. The haptic drive can be internallymounted on an inner side of the wearable device. The haptic drive can beinternally mounted on an inner side of the wearable device and behindthe inertial movement unit. The haptic drive provides a vibrationpattern in response to a sensory input stimulus selected from eyetracking, pupillometry, auditory cues, interoceptive awareness, physicalmovement, variations in body or ambient temperature, pulse rate,respiration, or combinations thereof. In another exemplary embodiment,the feedback device may also include a heads-up visual component, orother feedback devices that provide pupillary projection, distractingvisual blurring, removal, squelching, recoloring, or combinationsthereof.

According to further embodiments, the stereophonic bone conductiontransducer can be surface-mounted on an inner side of the wearabledevice, and the stereophonic bone conduction transducer can be in directcontact with a user's skull. The stereophonic bone conduction transducerprovides an auditory tone, a pre-recorded auditory guidance, real-timefiltering, or combinations thereof, in response to a sensory inputstimulus selected from eye tracking, pupillometry, auditory cues,interoceptive awareness, physical movement, variations in body andambient temperature, pulse rate, respiration, or combinations thereof.

According to further embodiments, the wearable device may furtherinclude an intervention means to providing relief to a user from thedistractibility, inattention, anxiety, fatigue, sensory issues, orcombinations thereof, wherein the intervention means is selected from analert intervention, a filter intervention, a guidance intervention, or acombination thereof. In exemplary embodiments, the intervention meansand the feedback means can be the same or different.

Various possible intervention means available to the user and deliveredby the wearable device are illustrated in the block diagram of FIG. 16 .As illustrated in FIG. 16 , following sensor(s) data stream delivery andmicroprocessor 312 comparison between ecological/environmental andphysiological/psychophysiological thresholds to real-time data, thoseevents deemed subject for interventional processing can be delivered toone of two discrete (or simultaneous) components: a haptic driver 313 ora bone conduction transducer 314. Pending a wearer's previously definedpreferences (stored in the microprocessor), one of four interventionalstrategies can be invoked: alert, filter, guidance, or combination.

Alert intervention: In the event of an ecological and/orphysiological/psychophysiological threshold's activation thatcorresponds to a wearer's preferences, a signal is delivered to: (i) thehaptic driver that provides a gentle, tactile vibration pattern toconvey information to the wearer that focus, anxiety, fatigue or relatedcharacteristics require their attention; and/or (ii) the bone conductiontransducer(s) that deliver an auditory/sonic message (e.g., pre-recordedtext-to-speech, beep tone, etc.) reinforcing the haptic with an auralintervention and set of instructions.

Filter intervention: In the event of an ecological and/orphysiological/psychophysiological threshold's activation thatcorresponds to a wearer's preferences and requires auditory filtering,digital audio signal processing delivers real-time and low-latency audiosignals that include corrected amplitude (compression, expansion),frequency (dynamic, shelving, low/hi-cut, and parametric equalisation),spatial realignment (reposition, stereo to mono) and/or phase correction(time delay, comb filtering, linear phase alignment). Though typicallydelivered to bone conduction transducers, these can be delivered tooptional wireless or wired hearing devices, including but not limited toearbuds, earphones, headphones, and the like.

Guidance intervention: Similar to alert intervention, the guidance byway of step-by-step instructions for re-alignment in focus, head sway,pupillary activity, anxiety, and fatigue coaching is provided to awearer. These pre-recorded, text-to-speech audio streams are deliveredto the bone conduction systems, which provide step-by-step instructionalintervention both privately and unobtrusively.

Combination intervention: Selectable by the wearer, a combination ofalert, filter and guidance interventions are provided depending upon thetriggering mechanism. For example, only sonic disturbances are addressedthrough filter intervention, while all other issues (attentional-focus,anxiety, etc.) can be intervened through haptic, text-to-speech alertsand long-form step-by-step guidance.

According to further embodiments, the wearable device may furtherinclude a power switch. The power switch can be located at a left sideof the wearable device and/or a right side of the wearable device. Thepower switch can be a recessed power switch.

In an embodiment, the wearable device may have a structure illustratedin FIG. 1 . As illustrated in FIG. 1 , the wearable device 10 can be inthe form of an eyeglass frame including a rim 109, left and rightearpieces, each having a temple portion 106 and temple tip 108 andscrews 103 and hinges 104 connecting the earpieces to the rim 109. Theframe may further include lenses 101, a nose pad 102, end pieces 107,and a bridge 105 connecting left- and right-sides of the frame. Thewearable device may have one or more sensors connected to the frame,including infrared pupillometry sensors 204, galvanic skin sensors 205,inertial movement units 206, wireless transceiver and A/D multiplexers208, microphones 201, and the like. The wearable device 10 may alsoinclude one or more feedback devices connected to the frame, includinghaptic drivers 203, bone conduction transducers 202, and the like. Thewearable device 10 may further include an optional wireless or wiredhearing device 209, and a power switch (not shown) and/or a rechargeablepower source 207.

Although the wearable device is depicted as an eyeglass frame in FIG. 1, it should be appreciated that the wearable device can be implementedusing a different type of head mount such as a visor or helmet. Otherexemplary embodiments of the wearable device can include, but are notlimited to, wrist worn devices, bone conduction devices, and the like,and any wearable device known in the field and adaptable to the methoddescribed herein can be used, and any of which may work in conjunctionwith a user interface described herein. In some cases, the wearabledevice can be implemented as a combination of devices (e.g., wearableeyeglasses, ring, wrist-worn, clothing/textile, and watch).

In some implementations, the wearable device can be communicativelycoupled to a mobile device (e.g., smartphone and/or other smart device)that controls operations of, works in concert with, and/or provides auser interface for change settings of the wearable device. For example,FIG. 19 depicts a wearable device system including a wearable device 10in communication with a mobile device 20, and a datastore 30. In thisexample system, the wearable device 10 communicates with mobile device20 over a wireless communication network. The wireless communicationnetwork can be any suitable network that enables communications betweenthe devices. The wireless communication network can be an ad-hoc networksuch as a WiFi network, a Bluetooth network, and/or a network using someother communication protocol. In some implementations, the wearabledevice 10 can be tethered to mobile device 20. In some implementations,the mobile device 20 processes sensor data collected by one or moresensors of wearable device 10. For example, the mobile device 20 candetermine, based on the processed sensor data, one or more interventionsto be applied using the wearable device 10 and/or some other device. Thedetermination can be based on one or more sensory thresholds 31 specificto a user wearing the wearable device 10. The interventions that areapplied can be based on one or more user-specific sensory resolutions 32specific to the user. Although the datastore storing thresholds 31 andresolutions 32 is illustrated in this example as being separate fromwearable device 10 and mobile device 20, in other implementations thedatastore 30 can be incorporated within wearable device 10 and/or mobiledevice 20.

Prior to use, the user can initiate personalization of the wearabledevice by identifying individual sound, visual andphysiological/psychophysiological thresholds using software integratedin the wearable device. Personalization can identify unique sensory,attentional-focus and anxiety/fatigue producing cues that a user findsdistracting particularly in educational, employment, social, and typicaldaily activities, and can be derived from the Participant PublicInformation (PPI) study described herein. The user-specific thresholdsare used to customize subsequent alerts, filters, and guidanceexperienced by the user when wearing the wearable device. Uponcompletion of the personalization process, the thresholds aretransmitted to the wearable device. The personalization thresholds maybe updated over time (e.g., periodically or dynamically) as the useradapts to stimuli or is presented with new stimuli.

In some implementations, the device may be configured via a mobileapplication (app), web-based application or other web-based interface(e.g., website). During the personalization process, the user can bepresented with a graphical user interface or other user interface viathe wearable device or via a smartphone or other device communicativelycoupled to the wearable device. For example, the wearable device orother device can include a processor that executes instructions thatcause the device to present (e.g., display) selectable controls orchoices to the user that are used to refine a set of thresholds, alerts,filters, and/or guidance in discrete or combined formats. In someimplementations, the personalization process can be conducted by thewearer of the device, a healthcare provider, or a caretaker of the user.For example, the personalization process can be conducted by running anapplication instance on the wearable device or other device andreceiving data corresponding to input from the wearer, health provider,or caretaker making selections (e.g., telemetry/biotelemetry). In somecases, different user interfaces and options can be presented dependingon whether personalization is conducted by the wearer, healthcareprovider, or caretaker.

In some implementations of the personalization process, a datastoreassociated with the wearable device may pre-store initializationtemplates that correspond to a particular set of thresholds (e.g.,sound, visual, and/or physiological/psychophysiological) and/or alerts,filters, and/or guidance. For example, templates corresponding topredominantly sonically sensitive wearers, predominantly visuallysensitive wearers, predominantly interoceptive sensitive wears,combination wearers, and the like can be preconfigured and stored by thesystem. During device initialization, the wearer can select one of thetemplates (e.g., the user is predominantly visually sensitive), and theconfigured parameters (e.g., thresholds, alerts, filters, and/orguidance) for the selected template can be further customized inresponse to additional user input. The additional user input can includeresponses to questions, or a selection of preferences as furtherdiscussed below.

In some implementations, each of the templates can be associated with atrained model that given a set of inputs (e.g., sensor readings from thewearable device, user thresholds, etc.) generates one or more outputs(e.g., alerts, filters, guidance, sonic feedback, visual feedback,haptic feedback) experienced by the user. The model can be trained andtested with anonymized historical data associated with users to predictappropriate outputs given sensory inputs and thresholds. Supervisedlearning, semi-supervised learning, or unsupervised learning can beutilized to build the model. During a personalization process, furtherdiscussed below, parameters of the model (e.g., weights of inputvariables) can be adjusted depending on the user's sensitivities and/orselections.

In some implementations of the personalization process, the user can bepresented with selectable preferences and/or answers to questions. Forexample, the personalization process may present the user withselectable choices relating to demographics (e.g., gender, age,education level, handedness, etc.) and sensitivities (e.g., audiopreferences, visual preferences, physiological/psychophysiologicalpreferences, alert preferences, guidance preferences, interventionranking preferences, and the like). Depending on the user's selections,a particular set of thresholds (e.g., sound, visual, and/orphysiological/psychophysiological), alerts, filters, and/or guidance maybe customized for the user and stored. For instance, based on the user'sselection of audio preferences, the system can be configured to performdigital signal processing of audio signals before audio is played to theuser to adjust the energy of different frequency ranges (e.g., bass,mid-range, treble, etc.) within the audible frequency band (e.g., 20 Hzto 20,000 Hz), the audio channels that emit sound, or othercharacteristics of audio. During configuration, the user can specify apreference for filtering (e.g., enhancing, removing, or otherwisealtering) low-range sounds, mid-range sounds, high-range sounds, softsounds, loud sounds, reverberant sounds, surround sounds, etc. Asanother example, a user can specify a preference for receiving alerts ofsounds having particular sonic characteristics (e.g., alert for loud,echoing, and/or surround sounds before they occur). As a furtherexample, a user can prefer that guided sounds have particularcharacteristics (e.g., soft-spoken words, gentle sounds) when the userbecomes anxious, unfocused, or sensitive.

In some implementations, the user's selected preferences and/or answersduring personalization can be used to build a model that given a set ofinputs (e.g., sensor readings from the wearable device, user thresholds,etc.) generates one or more outputs (e.g., alerts, filters, guidance,sonic feedback, visual feedback, haptic feedback) experienced by theuser. For example, a website or mobile app configurator accessed via auser login can generate one or more tolerance scores based on the user'sanswers to questions pertaining to visual, auditory, orphysiological/psychophysiological stimuli. The one or more tolerancescores can be used to initialize the model. The model can also beinitialized, modified and/or monitored by a specialist, healthcareprovider, or caretaker.

During personalization, a user can rank the types of interventions. Forexample, a user can rank and/or specify a preferred type of alert (e.g.,beep, haptic, voice, or some combination thereof), a preferred audiofilter (e.g., volume (compression, limiting), equalization (tone, EQ),noise reduction, imaging (panning, phase), reverberation (echo), imaging(panning, phase), or some combination thereof), a preferred type ofguidance (e.g., encouragement), and the like.

In some implementations, one or more sensors of the wearable device canbe calibrated during initialization of the device. A user can bepresented with an interface for calibrating sensors and/or adjustingsensor parameters. For example, the user can specify whether all or onlysome sensors are active and/or gather data, adjust sensor sensitivity,or adjust a sensor threshold (e.g., brightness for an optical sensor,loudness for an audio sensor) and what order of implementation they aredesired (e.g., alerts first, followed by guidance, followed by filters,etc.).

In some implementations, after an initial set of user-specificthresholds and alerts, filters, and guidance are set up for a user,validation of the configuration can be conducted by presenting the userwith external stimuli, and providing alerts, filters, and/or guidance inaccordance with the user-configured thresholds. Depending on the user'sresponse, additional configuration can be conducted. This validationprocess can also adjust sensor settings such as sensor sensitivity.

In implementations where a trained model is used to provide alerts,filters, and/or guidance, the model can be retrained over time based oncollected environmental and/or physiological/psychophysiological data.To save on computational resources and/or device battery life,retraining can be performed at night and/or when the system is not inuse.

FIG. 20 shows an operational flow diagram depicting an example method400 for initializing and iteratively updating one or more sensorythresholds and one or more interventions associated with a specificuser. In some implementations, method 400 can be implemented by one ormore processors (e.g., one or more processors of wearable device 10and/or mobile device 20) of a wearable device system executinginstructions stored in one or more computer readable media (e.g., one ormore computer readable media of wearable device 10 and/or mobile device20). Operation 401 includes presenting multiple selectable templates tothe user, the multiple templates corresponding to one or more sensorythresholds and one or more interventions. The multiple selectabletemplates can be presented via a GUI (e.g., using wearable device 10and/or mobile device 20). Operation 402 includes receiving datacorresponding to input by the user selecting one of the templates. Afteruser selection of one of the templates, the one or more sensorythresholds and one or more interventions associated with the templatecan be associated with the user. For example, the one or more sensorythresholds and one or more interventions can be stored in a datastore 30including an identification and/or user profile corresponding to theuser. As described above, operations 401-402 can be performed duringand/or after an initialization process.

Operation 403 includes receiving data corresponding to user inputselecting preferences. The preferences can comprise audio preferences,visual preferences, physiological/psychophysiological preferences, alertpreferences, guidance preferences, and/or intervention preferences.Operation 404 includes in response to receiving additional datacorresponding to additional user input selecting preferences, modifyingthe one or more sensory thresholds and the one or more interventionsassociated with the user. For example, the datastore thresholds andinterventions can be updated. As depicted, operations 403-404 caniterate over time as the user desires to further define thethresholds/interventions and/or as the user develops new preferences.

Operation 405 includes collecting sensor data and environmental datawhile the user wears the wearable device. Operation 406 includes inresponse to collecting the sensor data and/or environmental data whilethe user wears the wearable device, modifying the one or more sensorythresholds and the one or more interventions associated with the user.As depicted, operations 405-406 can iterate over time as the userutilizes the wearable device system to provide sensory relief. Thefrequency with which the one or more sensory thresholds and the one ormore interventions are updated in response to newly-collected data canbe configurable, system-defined, and/or user-defined. For example,updates can depend on the amount of data that is collected and/or theamount of time that has passed. In some implementations, operations405-406 can be skipped. For example, the user can disable updating thethresholds and/or interventions based on actual use of the wearabledevice.

In some implementations, the wearable device can be configured tocommunicate with and/or control IoT devices that present stimuli. Forexample, based on configured thresholds for a user, the wearable devicecan control the operation of smart devices such as networked hubs,networked lighting devices, networked outlets, alarm systems, networkedthermostats, networked sound systems, networked display systems,networked appliances, and other networked devices associated with theuser. For instance, the audio output (e.g., loudness and balance) of anetworked sound system and/or display output (e.g., brightness,contrast, and color balance) of networked display system can be alteredto meet individual sound or visual thresholds. To synchronizecommunication and operation between the wearable devices and IoTdevices, the devices can be linked to an account of the user, which canbe configured via an application running on a smartphone (e.g., nativehome control application) or other device (e.g., mobile device 20). Insome instances, behavior or one or more scenes for an IoT device can bepreconfigured based on the thresholds associated with the user. Thebehavior or scenes can be activated when the wearable device detectsthat it is in the presence (e.g., same room) of the IoT device.

By way of illustration, FIG. 21 depicts a wearable device systemincluding a wearable device 10 in communication with a mobile device 20that controls an IoT device 40 with a speaker 41. As another example,FIG. 22 depicts a wearable device system including a wearable device 10in communication with a mobile device 20 that controls an IoT device 50with a light emitting device 51. During operation, wearable device 10can use one or more sensors to collect a sensory input stimulus. Thissensory input stimulus can be transmitted to a mobile device 20 thatcompares the sensory input stimulus with one or more sensory thresholdsspecific to the user (e.g., thresholds 31) to determine an interventionto be provided to the user, to provide the user relief fromdistractibility, inattention, anxiety, fatigue, and/or sensory issues.

In the example of FIG. 21 , the sensory input stimulus can be generatedat least in part due to sound emitted by the speaker 41 of the IoTdevice 40. For example, the user can generate aphysiological/psychophysiological response to music and/or other soundsbeing played at a certain frequency and/or range of frequencies byspeaker 41. In that scenario, the intervention can include the mobiledevice 20 controlling IoT device 40 to filter, in the frequency domain,an audio signal such that sound output by speaker 41 plays in afrequency that does not induce the samephysiological/psychophysiological response in the user.

In the example of FIG. 22 , the sensory input stimulus can be generatedat least in part due to light emitted by the light emitting device 51 ofthe IoT device 50. For example, the user can experience discomfort whenthe output light is too bright or too cool (e.g., >4000K) in colortemperature. This discomfort can be measured using the sensory inputstimulus collected by the one or more sensors of the wearable device 10.In that scenario, the intervention can include the mobile device 20controlling IoT device 50 to filter an optical signal of light device 51to lower a brightness and/or color temperature of light output by thelighting device 51.

Although the foregoing examples depict the mobile device 20 ascommunicating with and controlling IoT devices 40, 50, it should beappreciated that these functions can instead be performed by thewearable device 10.

The wearable device can include various user interface components,including but not limited to mobile phones, laptops, tablets, desktopcomputers, and the like, and any user interface known in the field canbe used. In some implementations, the wearable device can besynchronized with a smartphone. For example, the wearable device can beconfigured to accept calls, adjust call volume, present notificationsounds or vibrations, present ringtones, etc. The wearable device can begranted access to user contacts, text messages or other instantmessages, etc. In some instances, the intensity of sounds or vibrations,or the pattern of sounds or vibrations, presented via mobile integrationcan depend on configured thresholds of the user. The initialconfiguration and personalization of the wearable device can beconducted via an application installed on a smartphone or other device.

The wearable device can include one or more network interfaces (e.g.,WiFi, Bluetooth, cellular, etc.) for communicating with other networkeddevices and/or connecting to the Internet. For example, a WiFi interfacecan enable the wearable device to select and communicatively couple to alocal network, which can permit communication with IoT devices.Bluetooth can enable pairing between the wearable device and asmartphone or other device.

The wearable device can include or communicatively couple to one or moredatastores (e.g., memories or other storage device) that are accessedduring its operation. Storage can be local, over a network, and/or overthe cloud. Storage can maintain a record of user preferences, userperformance, trained models, and other data or instructions required tooperate the device.

In an exemplary embodiment, a wearable device is operated as describedherein. The wearable device can remain in passive mode, i.e.,non-operating mode, before it is worn by a user. This can optimizebattery life.

Once in active mode, i.e., when the wearable device is in an operatingmode, the wearable device detects and responds to one or more sensorycues selected from a myriad of sensory cues received and detected by oneor more sensors located on the wearable device. Such sensory cues caninclude environmental and physiological/psychophysiological signals, andthe like. The wearable device also provides additional and appropriateresolution in response to the sensory cues via alerts, filters, andguidance to the user whenever personalized thresholds for the use areexceeded. Thresholds and interventions can be iteratively set, adjusted,muted, and otherwise cancelled at any time and throughout the use of thewearable device by the user by returning to the computer/application.

Various types of sensory cues can be received and detected by thewearable device, including visual, auditory, andphysiological/psychophysiological cues, but are not limited thereto. Inan exemplary embodiment, visual distractions can be detected via eyetracking and pupillometry monitored by in infrared sensor that can besurface mounted on an inner side of the wearable device, for example, atan intersection of frame rim/right hinge temple and aimed at an eye ofthe user, for e.g., the right eye or the left eye or both. In anexemplary embodiment, auditory distractions and audiometric thresholdscan be monitored by subminiature and wired electret microphones that canbe surface mounted on an outer side of the end pieces, near theintersection of the frame front and temples. In an exemplary embodiment,physiological/psychophysiological distractions, interoceptive thresholdsand user head sway can be monitored by a galvanic skin sensor that issurface-mounted on an inner side of the left earpiece and in directcontact with the skin just above the user's neckline and/or an inertialmovement unit that is internally mounted on an inner side of thewearable device, and can be located behind an ear piece. The variousdetection components described herein are merely exemplary, and anysuitable components can be used.

Various types of resolutions (interventions or digital mediations) canbe provided to the user in response to the sensory cues received by thewearable device. The resolutions may include visual, auditory andphysiological/psychophysiological resolutions, but are not limitedthereto. In an exemplary embodiment, the visual resolutions can bedelivered through a haptic driver that can be internally mounted on aninner side of an ear piece and behind an inertial movement unitintersection of frame rim/right hinge temple. Visual resolutions can beprovided via unique vibrations associated with optical distractions whena pupillary or inertial/head sway threshold is detected. In anotherexemplary embodiment, the visual alerts can be delivered by astereophonic bone conduction that can be surface mounted on an innerside of wearable device, for example, at both temples midway between thehinges and temple tips and coming into direct contact with the user'sleft and right skull in front of each ear and provides either a beeptone and/or pre-recorded spoken guidance in the event a pupillary orinertial/head sway threshold is detected. In another exemplaryembodiment, auditory resolutions can be delivered to the user through asingle, haptic driver by providing uniquely coded vibrational alerts inthe event a sonic threshold is detected and/or through a bone conductiontransducer that provides both beep tone, pre-recorded spoken guidanceand/or real-time filtering using digital signal processing (DSP) forthose distracting, environmental audiometric events (e.g., compression,equalization, noise reduction, spatial panning, limiting, phaseadjustment, and gating) when a sonic threshold(s) is/are detected andcan be processed according to user's personalization settings. Forexample, in an exemplary embodiment, real-time digital audio streamsrecorded by microphones connected to the wearable device provide themicroprocessor with audio data that undergo system manipulation toachieve a predetermined goal. As described, the DSP produces feedback inthe form of altered audio signals (the filtered intervention) thatameliorates volume (amplitude, compression, noise reduction), tonal(equalization), directional (spatial, etc.). As another example,guidance may include one or more tonal alerts retrieved from adatastore.

In an exemplary embodiment, the device can be configured to boostcertain audible frequencies depending on the user's age or hearing. Forexample, the device can boost low, mid, and/or high frequenciesdepending on the user's age and/or hearing profile. In some cases, thedevice can execute instructions to provide a hearing test to generatethe hearing profile. A control can be provided to enable or disablesound boosting.

In an exemplary embodiment, physiological/psychophysiologicalresolutions can be delivered to the user through a haptic drivermentioned above and by providing uniquely coded vibrational alerts inthe event a physiological/psychophysiological, anxiety, fatigue or otherinteroceptive thresholds are detected and/or through a bone conductiontransducer, which provides both beep tone, and/or pre-recorded spokenguidance for similar threshold alert and guidance. The variousresolution components described herein are merely exemplary, and anysuitable components can be used.

In an exemplary embodiment, the wearable device may also include aninternally mounted central processing unit, that may further includesubminiature printed circuit boards combined with a self-containedconnected and rechargeable power source, wireless transceiver andanalog/digital multiplexers reside within both earpieces and provideevenly weighted distribution to wearer.

As described herein, the comparing means compares the sensory inputstimulus recorded by the one or more sensors with the database of one ormore user-specific sensory thresholds to obtain a sensory resolution fora user. The comparing means performs the afore-mentioned functions asfollows.

The user-specific thresholds can be obtained by having the user completea decision-tree styled survey (similar in scope to the survey describedin the Sustained Attention to Response Test (SART) protocol describedherein), and then a microprocessor measures the user-specific thresholdsagainst ecological and physiological/psychophysiological data streams todeliver appropriate intervention assistance. In an exemplary embodiment,the user-specific thresholds can dynamically change using a machinelearning capability.

An exemplary embodiment of how input stimulus is compared to stored datato generate user-specific interventions is illustrated via a blockdiagram in FIG. 16 , but this application is not limited thereto. In theexemplary embodiment illustrated in FIG. 16 , six components make up thewearable device's input section and include: an optical module 301; aninertial measurement unit (IMU) 304; an audio sensor 305; a galvanicmodule 306; a temperature sensor 309; and a biopotential analogue frontend (AFE) 310. In combination, these components deliver both ecological(environmental) and physiological/psychophysiological data to a sensorhub 311 (multiplexer), and the data is processed (typically throughwireless, bi-directional communication, though it can be directlyconnected) with the system's microprocessor (e.g., ARM Cortex) for rapidanalysis and comparison to existing thresholds, characteristics, anduser-preferences. The microprocessor 312 (e.g., ARM microprocessor) thendelivers the appropriate commands for interventional activities to beprocessed by those related system components as described herein. Thesix components are further described as follows.

The optical module 301 includes: (i) an inward facing pair of infraredsensors 302 that monitor pupillary response, portending to a user'sfocus and attentional lability; and (ii) a single outward facing sensorto determine ecological/environmental cues of a visual nature. A tunedoptical AFE 303 provides the appropriate pupillary data stream forprocessing and simultaneously provides an environmental data stream forimage recognition allowing the microprocessor 312 to determine visualenvironmental cues for which the user is responding. In both cases,image recognition (whether pupillary response, saccades, computerscreen, books, automobile roadways, office/academic surroundings, etc.)rely on a computer vision technique that allows the microprocessor 312to interpret and categorize what is seen in the visual data stream. Thistype of image classification (or labelling) is a core task andfoundational component in comparing real-time visual input to alibrary/catalogue of pre-labelled images that are interpreted and thenserve as the basis for an intervention, provided that the user'sthresholds are exceeded (or unsurpassed).

The IMU 304 measures and reports a body's specific force (in this case,the user's head/face). It also provides angular rate and orientationusing a combination of accelerometers, gyroscopes, and magnetometers todeliver a data stream relating to the user's head sway and attentionalfocus, when compared and contrasted to the optical AFE 303 and processedsimilarly against pre-labelled and classified data. In someimplementations, the IMU can include a 3-axis gyroscope/accelerometer.

Similar in scope to the optical module 301, the audio sensor(s) 305provide environmental data streams of a sonic nature which can becompared to known aural signatures that have been labelled and availablefor computer micro processing. The aural signatures that reachfrequency, amplitude, spatial, time-delay/phase and similaruser-selected thresholds could then be delivered for interventionalprocessing.

Both the galvanic module 306 and temperature sensor 309 providephysiological/psychophysiological and ambient/physiological data streamsthat measures the wearer's electrodermal activity (EDA), galvanic skinresponse (GSR), body and ambient temperature. These are utilized incombination with the biopotential AFE 310 resulting in real-time andcontinuous monitoring of the wearer's electrical skin properties, heartrate, respiratory rate, and blood pressure detection. Like the previoussensors, all are timestamped/synchronized for microprocessor processing,analysis, labelling/comparison and interventional activation.

The biopotential AFE 310 provides electrocardiogram (ECG) waveforms,heart rate and respiration, which in turn, feeds forward to themicroprocessor 312 to assist with a user'sphysiological/psychophysiological state, processing, and attentionalfocus/anxiety/fatigue intervention(s).

An additional block diagram providing additional microprocessor details(ARM processor) is illustrated in FIG. 17 .

A catalogue of user-specific cues and resolutions can be stored in adatabase in communication with the software stored in and executed fromthe wearable device and the control program/app, and available formachine learning purposes providing the application and hardware withever-increasing understanding of user environments and physiology cues,alerts, filters, and guidance. An artificial intelligence (AI) algorithmcontinuously processes user personalization, input cues and uniquelycrafted resolutions to further narrow and accurately predict and respondto physiological/psychophysiological input and responses. This machinelearning and AI algorithms increase user training and promote greaterautonomy, comfort, alertness, focus and mental health. The catalogue isavailable for user and professional analyses, data streams and progressreports are available for clinical study, medicalpractitioner/telemedicine, evaluation, and further review.

In some implementations, the wearable device preferences can be modifiedby the user to optimize device battery life. For example, the device canbe configured to operate in a power saving mode that conserves batterylife by making the sensor(s) less sensitive, limits power for less usedoperations, or otherwise operates in a manner to maximize battery life.Alternatively, the user can have the option of selecting an enhancedprocessing mode that emphasizes processing (e.g., makes the sensor(s)more sensitive) but uses more battery per unit of time.

In some implementations, the wearable device can be associated with anapplication that provides diagnostic data relating to the user, system,or for a caretaker/healthcare professional. For example, user diagnosticdata can include user preferences, user responsivity, and generatedissues and warnings. System diagnostic data can include environment anddevice responsivity, and issues and warnings. Caretaker/healthcareprofessional diagnostic data can include user efficacy performance(e.g., sonic, visual, or interoceptive), and any areas of concern suchas wearer guidance or device guidance.

As alluded to above, real-time filtering of audio signals can beimplemented in response to collecting sensory data from one or moresensors of the wearable device. As contrasted with adjusting time oroverall amplitude of the signal experienced by the listener, thisfiltering can take place in the frequency domain and affect at least acenter frequency (Hz), a cut or boost (dB), and/or a width (Q). Forexample, all low frequency hum associated with a real-time detection ofmachinery and/or light ballasts in an environment can be eliminatedand/or otherwise reduced, minimized and/or mitigated. This can beimplemented by adjusting, in real-time, the offending and nearbyfrequencies, either with a band-pass, or low cut, high pass filter, withspecific adjustments fine-tuned to the user's personalized profile. Insome implementations audio filtering can also apply to additionaldomains, including time, amplitude, and spatial positioning (e.g., tofilter distracting sounds that modulate from a given direction).

While some implementations have been primarily described in the contextof modifying and/or filtering distracting sounds (i.e., audiointerventions), the technology described herein can implement a similarset of interventions related to visual stimuli, either separately or incombination with other types of stimuli. For example, interventions suchas alerts, guidance, and/or combinations without filtering mediationscan be implemented. As further described below, visual interventions canbe based upon pupillary response, accelerometers, IMU, GSR detection,and/or video of the wearer's environment. In some implementations,identified visual interventions can work in concert with audiomodifications.

FIG. 23 depicts an example wearable device 500 that can be utilized toprovide visual interventions, in accordance with some implementations ofthe disclosure. As depicted, wearable device 500 can include the sensorsand/or transducers of wearable device 10. To support certain visualinterventions, wearable device 500 also includes a camera 550 anddisplay 551. As such, wearable device 500 is implemented as a wearableHMD. Although a glasses form factor is shown, the HMD can be implementedin a variety of other form factors such as, for example, a headset,goggles, a visor, combinations thereof and the like. Although depictedas a binocular HMD, in some implementations the wearable device can beimplemented as a monocular HMD.

Display 551 can be implemented as an optical see-through display such asa transparent LED and/or OLED screen that uses a waveguide to displayvirtual objects overlaid over the real-world environment. Alternatively,display 551 can be implemented as a video see-through displaysupplementing video of the user's real world environment with overlaidvirtual objects. For example, it can overlay virtual objects on videocaptured by camera 550 that is aligned with the field of view of theHMD.

The integrated camera 550 can capture video of the environment from thepoint of view of the wearer/user of wearable device 500. As such, asfurther discussed below, the live video/image feed of the camera can beused as one input to detect visual objects that the user is potentiallyvisually sensitive to, and trigger a visual intervention.

In some implementations, real-time overlay interventions can beimplemented whereby visual objects and/or optical interruptions aremuted, squelched, minimized, mitigated and/or otherwise removed from awearer's field of vision. In some implementations, the system'stransducer components (e.g., microphones and/or outward facing optics)can be used in concert with on-board biological sensors and/orprojection techniques that train/detect, analyze/match/predict, and/ormodify optical cues and/or visible items that correlate to a wearer'svisual sensitivity, attention, fatigue and/or anxiety thresholds. Insome implementations, disrupting visual, optical, and/or related scenerycan be filtered in real-time such that a wearer does not notice thatwhich is distracting.

In particular embodiments, one or two types of real-time opticalenhancement (REOPEN) algorithms can be implemented to detect, predict,and/or modify visual inputs that decrease distraction/mental healthissues and increase attention, calmness, and/or focus. The algorithmscan provide real-time (i) live-editing of visual scenes, imagery and/orobject and advanced notification for distracting optics that match auser's visual profile; and/or, (ii) live-modification of visualdistractions without advanced notification. Interventions that can bedelivered in real time using a REOPEN algorithm are illustrated by FIGS.24A-24C, further discussed below.

In one embodiment, a Realtime Optical Enhancement and Visual AprioriIntervention Algorithm (REOPEN-VAIL) can be implemented to train/detect,analyze/match/predict, and/or modify visual items that a user deemsdistracting (e.g., based upon a previously-described and/or createdpersonalized preferences profile) and compares these to prior and/orcurrent physiological/psychophysiological responses to the environment.Upon detecting a threshold crossing and/or match between interoceptivereactivity (egocentric) and visual cue (exocentric video) detection,REOPEN-VAIL can provide iterative analysis, training, enhancement,contextual modification, and/or advanced warning of optical distractionsprior to the wearer's ability to sense these visual and/or relatedphysiological/psychophysiological cues.

In one embodiment, a Visual A Posteriori Intervention Algorithm (VASILI)can be implemented to use multimodal learning methods to train/detect,analyze/match and/or modify visual items that previously a user deemsdistracting (e.g., based upon previously described or createdpreferences, prior and/or current physiological/psychophysiologicalresponses, etc.). In the case of VASILI, as contrasted with REOPEN-VAIL,the optics provide contextual modifications without advanced warning ofdistractibility in the form of interventions that are deliveredfollowing the system's identification of either ecological and/or thewearer's physiological/psychophysiological cue(s), in real-time afterthe user has been exposed to the visual distraction, and as part of aniterative process that can serve as a basis for future training,sensing, and/or apriori algorithms.

Various interventions can potentially be delivered in real time using aREOPEN algorithm. For example, as depicted by FIG. 24A, in response todetection of a certain visual object (e.g., a distracting visual objectand/or visual anomaly), haptic alerts, tone alerts, guidance alerts,combinations thereof and the like can be delivered to notify the wearer.The guidance alerts can provide user-selectable verbal instructions ofanticipated visual distraction and/or coaching to intervene withcontinued focus, calmness, and/or attention. The aforementioned guidancealerts can be implemented as visual and/or text based guidance that isviewable to the wearer, via a displayed (e.g., using display 551) userselectable system visible to one and/or both eyes and/or sightlines tointervene with continued focus, calmness, attention, combinationsthereof and the like. (e.g., FIG. 24B).

In some implementations, visual distractions (e.g., certain objects,faces, etc.) can be rendered such that they appear as opaque and/orobscure. This blurring effect can blur the identified, distracting,and/or otherwise offending image as a user-selectable and/or predefinedintervention affecting what the wearer sees. (e.g., FIG. 24B).

In some implementations, a visual scene can be rendered with a modifiedbackground, eliminating the visual distraction, and/or identified image.The system can interpolate nearby images to the distracting objectand/or replicate the background by overlaying a “stitched” series ofimages that naturally conceal and/or suppress the sensory effects of theoffending optics, all of which are user-selectable. (e.g., FIG. 24A).

In some implementations, a predetermined, user-selectable emoticonand/or place-holder image can be rendered that camouflages thedistracting optic and/or visual disruption. (e.g., FIG. 24B).

In some implementations, a visual distraction can be rendered with amodified color palette and/or related pigmentation can be modified touser-preference to reduce the effects of distraction, sensitivity,focus, anxiety, and/or fatigue, and/or combinations thereof and the like(e.g., FIG. 24C).

In some implementations, a visual distraction can be rendered withedited brightness and/or sharpening of images that are user-selectableas either muted and/or modified visuals (e.g., FIG. 24C).

In some implementations, a visual distraction can be rendered with anedited size such that images are augmented and/or modified such theybecome more prominent, larger, and/or highly visible (e.g., FIG. 24C).

The principles of the present invention includes certain exemplaryfeatures and embodiments, and effects thereof, will now be described byreference to the following non-limiting examples.

FIG. 25 depicts one particular example of a workflow that uses a REOPENalgorithm to provide interventions, in real-time, in a scenario wherethere is a singular distracting visual source (e.g., birds flying acrossthe sky and causing the individual to become unfocused from work). Inthis example, the algorithm is implemented using a convolutional neuralnetwork (CNN). As depicted, distracting stimuli can be visualized by theindividual wearing a wearable device (e.g., wearable device 500) thatcaptures cues and then processes and trains on that data (e.g.,environmental and/or psychophysiology). Convolution layers of the CNNare formed and/or iteratively examined, for example, visual data thattriggers the distraction—or physiology such as pupillary movement,including edges, shapes, and/or directional movement. Connected layersdigitize the prior convolutional layers. This can be repeated formultimodal data types such that when layers correlate to pre-defined“eyes on target”, a learned state of focused activity can be recorded.Conversely, when pupillary movement is uncoordinated with a targetand/or activity, a separate learned event can be memorialized and/ortagged as unfocused activity, resulting in delivery of a digitalmediation until a “focused” condition is observed.

For example, in the case of a REOPEN-VAIL algorithm, an aprioriintervention could be one that has already been trained on the flowdepicted in FIG. 25 , and then sensed by the system pursuant to asimilar external cue and/or an early reflection of pupillary unfocusedprediction. This could generate an alert prior to the actual long-termindividual state in an attempt to mediate prior to distractibility. Inthe event of a failure in the REOPEN-VAIL (e.g., the individualcontinues to remain out of focus for a period of time, e.g., greaterthan about 5 seconds or otherwise dictated by the personalizedparadigm), the posteriori flow could repeat, this time offeringmediations consisting of alerts, guidance, and potentially filtering tomute, eliminate, mitigate, minimize and/or otherwise modify theoffending distraction.

PPI Study

A PPI study was conducted to identify dependent/thematic variables anddependent/demographic factors related to the utilization of the wearabledevice. The PPI participants included verbally able, autistic andneurotypical adolescents and adults aged 15-84. All participants hadintelligence in the normal or above average range and the majority wereliving independent lives, i.e., study participants did not fall into thegeneral learning disabilities range. Participants providedhealth/medical conditions and disability information relevant to theiropinions about distractibility, focus and anxiety at both school andwork. Before the study, participants provided informed consent alongwith a verifiable ASC diagnosis, where applicable. All participants wereinvited to take part in a Focus Group, User Survey Group or both.

The Focus Group included 15 participants, ages 17-43, and participantsstudied distractibility and attentional focus. The main task of theFocus Group was to comment on sensory issues and provide input into thedesign of a user survey to ensure relevance to autism and adherence toan autism-friendly format.

The User Survey Group included 187 participants, ages 18-49, andprovided first-person perspectives on distractibility and focus whilegathering views and opinions of which aspects of technologicalaid/support would be most welcomed and have the biggest impact onsensory, attentional, and quality of life issues.

Embedded within the PPI study was a Lived Experience Attention AnxietySensory Survey (LEA²Se) developed for participant expression and used asa preparatory point to discuss focus, distractibility, anxiety, sensoryand attentional difficulties, and needs. The LEA²Se participants wereencouraged to specify interests, attitudes, and opinions about receivingtechnology supports. This study was dispensed online.

Pre-Trial Battery Examination (PTBE)

196 autistic participants were recruited via opportunity sampling. Afterexclusion, 188 participants were left (109 males, 79 females), of which12.2% were 18-20 years old, 21.2% were 21-29 years, 60.8% were 30-39years and 5.8% were 40-49 years old. For the purpose of the PTBE,variables that tap into different aspects of an autistic individual'sexperience were designed. After identifying the variables of interest,questions which addressed these variables were allocated a numericalvariable. Some questions were not allocated to any variable, andtherefore were not analyzed in this study. Table 1 shows the resultantvariables, the number of questions that fell under each variable, and anexample of the type of questions used to investigate that variable. Eachparticipant received a score for each of these variables, which wascalculated by averaging their responses to the questions that fell underthat variable. The variables are mutually exclusive (i.e., no questionwas included in the computation of more than one variable). To assessvalidity of the variables, inter-item correlations for each variablewere investigated, all of which had a Cronbach's alpha greater than0.80, which demonstrates high internal validity.

TABLE 1 No. of Cronbach’s Variable Items Alpha Sample QuestionSensitivity 9 .871 “I have considered abandoning Impact (SI) orinterrupting my job/employment or academic studies because ofsensitivity to my environment” Anxiety 25 .947 “Certain sounds, sightsor Proneness (AP) stimuli make me feel nervous, anxious or on edge”Distractibility 9 .839 “I often begin new tasks and leave them Quotient(DQ) uncompleted” Technology 11 .885 “I think I would enjoy owningTolerance (TT) a wearable device if it helped reduce anxiety, lessendistraction or increase focus at work, school, seminars, meeting orother locations” Visual Difficulty 4 .919 “I have difficulty in brightQuotient (VDQ) colourful or dimly lit rooms” Sound Difficulty 6 .821 “Ifind sounds that startle me Quotient (SDQ) or that are unexpected as . .. ” (distracting-not distracting) Physiological 3 .925 “My sensitivitysometimes Difficulty causes my heart rate to Quotient (PDQ) speed up orslowdown”

Various pilot outcomes using benign data are depicted in FIGS. 2 and 3 .These graphics report sensitivity across three modalities (visual, auraland anxiety) along with wearable interest among ASC participants forvisually distracting stimuli. Kruskal-Wallis H testing on the studypopulation indicated:

a statistically significant difference in sensitivity impact, anxietyproneness, distractibility quotient, technology tolerance, visualdifficulties and physiological/psychophysiological difficulties, but nostatistically significant difference in sound difficulties, based onage;

a statistically significant difference in sensitivity impact and sounddifficulties, but no statistically significant difference in anxietyproneness, distractibility quotient, technology tolerance, visualdifficulties, physiological/psychophysiological difficulties, based ongender;

a statistically significant difference in sensitivity impact, anxietyproneness, distractibility quotient, technology tolerance, sounddifficulties, visual difficulties and physiological/psychophysiologicaldifficulties, based on education level; and

a statistically significant difference in sensitivity impact, anxietyproneness, distractibility quotient, technology tolerance, sounddifficulties, visual difficulties, but no statistically significantdifference in physiological/psychophysiological difficulties, based ondifferent employment levels.

Sustained Attention to Response Test (SART)

A SART study was conducted subsequent to the PPI study and PTBE. Thestudy included online testing designed to test sensory issues affectingparticipants diagnosed or identifying with ASC. Specifically, this studyexamined a subset of components within a wearable prototype to answertwo questions: (i) is it possible to classify and predict autisticreactivity/responsiveness to auditory (ecological) disturbances andphysiological/psychophysiological distractors when autistic individualsare assisted through alerts, filters and guidance; and (ii) can theexploration of Multimodal Learning Analytics (MMLA) combined withsupervised artificial intelligence/machine learning contribute towardunderstanding autism's heterogeneity with high accuracy therebyincreasing attentional focus whilst decreasing distractibility andanxiety.

This study is grounded in Attention Schema, Zone of ProximalDevelopment, Multimodal Discourse Analysis and Multimodal LearningAnalytics theories, and makes use of both evaluator-participatory anduser-participatory methodologies including iterative development andevaluation, early-user integration, phenomena of interest and persistentcollaboration methodologies.

In an exemplary study protocol, baseline testing and related scores arederived both procedurally on pre- and post-subtests to create putative,cognitive conflicts during subtests that may result in a hypothesizedand measurable uptick in both distractibility and anxiety.Simultaneously, this upsurge will likely pool with diminished focus andconical attentional performance. Finally, and during the lattersubtests, a “confederate” (human wizard) will present a collection ofhand-crafted alerts, filters and guidance. These will emulate theoperation of the wearable intervention by offsetting andcounterbalancing distracting aural stimuli. To reduce fatigue effect,these interventions will either exist in counterbalanced, randomized andpossibly multiple sessions. Alternatively, a combination of alerts,filters and guidance will be provisioned to lessen overall length of theexperiments, as shown in Table 2 below and illustrated in FIG. 4 .

The delivery of isolated and permutated support may produce broadmeasures of test responses. Mixed method (qualitative and quantitativeevaluations) combined with participants' overt behaviors obtainedthrough audio and video recordings may provision coding and analysiswith sample accuracy synchronization to the systems software. Dependingupon sample size and time constraints, this design considers post-hocvideo analyses (e.g., participant walk-through) in either a structuredor liberal form. These examinations may help facilitate recall,precision and provide further understanding of anxiety and otherepisodic testing moments.

TABLE 2 Test Description Baseline SART Standard sustained attention testwithout sonic disturbances. Subtests including Subtest I.: Standardsustained attention test interventions (SART) with sonic disturbance.Subtest II.: SART with combined filters and sonic disturbances. SubtestIII.: SART with combined alerts and sonic disturbances. Subtest IV.:SART with combined guidance and sonic disturbances. Subtest V.: SARTwith combined filters, alerts, guidance and sonic disturbances.Follow-on baseline Standard sustained attention test SART without sonicdisturbances.

This study tests a sub-system mock-up using multimodal, artificialintelligence-driven (MM/AI) sensors designed to provide personalizedalerts, filters, and guidance to help lessen distractibility and anxietywhilst increasing focus and attention by enhancing cognitive loadrelated to unexpected ecological and physiological/psychophysiologicalstimuli. The study uses a series of online experiments in which thewearable's operation is simulated by a confederate, human operator. Thisstudy proposes within-subjects, two-condition SART employing multimodalsensors during which a user's performance is measured (Robertson, I. H.,Manly, T., Andrade, J., Baddeley, B. T., & Yiend, J. (1997). ‘Oops!’:Performance correlates of everyday attentional failures in traumaticbrain injured and normal subjects. Neuropsychologia, 35(6), 747-758).Importantly, tasks were performed, and data was collected, with andwithout the effects of distracting sonic stimuli (the singular modality)accompanied by various combinations of advanced alerts, audio filteringand return-to-task guidance. This phase of the study included forty (40)participants, including 19 autistic participants and 21 non-autisticparticipants.

The classic SART paradigm, which is regarded as an exemplar of both highreliability and validity, requires participants to withhold pressing acomputer key during the on-screen appearance of a target image. Thisstudy modifies SART by flipping the keystroke sequence; that is, ratherthan holding a key “down” throughout the majority of the test, theassigned key was depressed only when a target appeared. This providedgreater reliability and reproducibility when testing at distance andonline (Anwyl-Irvine, A. L., Massonid J., Flitton, A., Kirkham, N. Z.,Evershed, J. K. (2019). Gorilla in our midst: an online behaviouralexperiment builder. Behavior Research Methods). Performance on the SARTcorrelates significantly with performance on tests of sustainedattention. Research indicates that SART does not, however, correlatewell to other types of attentional measures, “supporting the view that[SART] is indeed a measure of sustained attention” (Robertson et al.,1997, 747, 756). This study employs SART specifically because studieslike Robertson's corroborate that this methodology is fundamentallyimpervious to effects of age, estimated intelligence scoring or otherintellectual measures.

Additionally, and within this study, SART tasks are performed, and datais collected, with and without the effects of distracting sonic stimuli.This modality serves as both the singular and irrelevant foil, whenaccompanied by various subtest combinations of advanced alerts, audiofiltering and return-to-task guidance models. These combinations serveas the intervention(s). The study subtests exploit visual search oftargets against competing and irrelevant foils (e.g., alpha-numeric).Supplementing these textual targets with additional contestingmodalities (e.g., sonic foils and interventions) makes this SART studynovel compared to previously-conducted studies. SART requiresparticipants to “actively inhibit competing distractors and selectiveactivation of the target representation. Memory factors are minimal inthese tasks, as the targets are simple and are prominently displayed tosubjects in the course of testing” (Robertson, I. H., Ward, T.,Ridgeway, V., & Nimmo-Smith, I. (1996). The structure of normal humanattention: The Test of Everyday Attention. Journal of the InternationalNeuropsychological Society, 2(6), 525, 526). Though reaction time andother temporal measures are considered in developing participant scores,this study rules out the possibility that subtests only measure samplingspeed of processing as qualitative mental health measures are alsointegrated.

The first PPI study and PTBE facilitated a deeper understanding of thelived experiences of autistic individuals' and their focus,distractibility and anxiety concerns with a particular focus onlater-life, educational and workplace experiences. The PPI study andPTBE also provided information regarding a potential decrease in bothanxiety and sensitivity as autistic people age, and that these trendsdiffer within specific modalities. Stability is achieved across variousages for a sonic variable but varies for both visual andphysiological/psychophysiological variables. Further, anxiety andsensitivity may not relate across gender. And while there are downwardaging trends in both technology tolerance and distractibility, there isvariation in ages 30-39 perhaps due to the massive size of thisparticular sample.

The study design is rooted in a SART/WoZ design and includes onlineexperiments whereby system operations were simulated by a human operatorarmed with prior, hand-crafted interventions and scripts that supportparticipants' testing (Bernsen, N. O., DybkjoTr, H., & Dybkjor, L.(1994). Wizard of oz prototyping: How and when. Proc. CCI Working PapersCognit. Sci./HCl, Roskilde, Denmark). The Wizard of Oz (WoZ) studydesign provides economical and rapid implementation and evaluation, andhas gained academic acceptance and popularity for decades. (Bernsen etal., 1994); (Robertson et al., 1997); (Fiedler, A., Gabsdil, M., &Horacek, H. (2004, August). A tool for supporting progressive refinementof wizard-of-oz experiments in natural language. In Internationalconference on intelligent tutoring systems (pp. 325-335)); (Maulsby, D.,Greenberg, S., & Mander, R. (1993, May). Prototyping an intelligentagent through Wizard of Oz. In Proceedings of the INTERACT'93 and CHI'93conference on Human factors in computing systems (pp. 277-284)). Thesesupports may lessen distractibility/anxiety whilst increasing attentionby enhancing cognitive load related to unexpected stimuli. WoZ proposesa within-subjects, two-condition SART employing multimodal sensorsduring which a user's errors of commission, errors of omission, reactiontime, state-anxiety, and fatigue levels are computed. (Burchi, E., &Hollander, E. (2019). Anxiety in Autism Spectrum Disorder); (Ruttenberg,D. (2020). The SensorAble Project: A multi-sensory, assistive technologythat filters distractions and increases focus for individuals diagnosedwith Autism Spectrum Condition. MPhil/PhD Upgrade Report. UniversityCollege London).

Memory factors are minimized in SART testing, as visual tasks are modestand tried out by participants prior to testing. Though intervallicmeasures are included in scoring participant performance, there areother critical metrics resulting from both qualitative and quantitativescoring. As mentioned in Robertson (1996), these do not create cognitiveburdens of similar dynamics and characteristics; therefore, they do notconstitute a myopic or simplified sampling speed of processing measure.Further, this study separates visual sustained tasks from auditorydistractions, which in turn avoids cross-modality and interferenceconcerns.

The study utilized t-test/correlation point biserial models (Faul, F.,Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexiblestatistical power analysis program for the social, behavioral, andbiomedical sciences. Behavior Research Methods, 39, 175-191).Computations were based upon a given a, power and effect size.Generally, T-tests are calculations that inform the significance of thedifferences between groups. In this case, it answers whether or not thedata between autistic and non-autistic scores (measured in means) couldhave happened by chance. Alpha (a) is a threshold value used to judgewhether a test statistic is statistically significant, and was selectedby the inventor (typically 0.05). A statistically significant testresult (p=probability and p<0.05) indicates that the test hypothesis isfalse or should be rejected, and a p-value greater than 0.05 means thatno effect was observed. The statistical power of a significance test(t-test) depends on: (i) the sample size (N), such that when Nincreases, the power increases; (ii) the significance level (a), suchthat when a increases, the power increases; and (iii) the effect size,such that when the effect size increases, the power increases.

Half the sample included neurotypical participants and half identifiedas or possessed an ASC diagnoses. All participants utilized pre/post WoZmanipulations. Baseline testing and related scores were derived bothprocedurally on pre- and post-subtests. Putative, cognitive conflictsduring subtests that may result in a hypothesized and measurable uptickin both distractibility and anxiety were created. Simultaneously, thisupsurge likely pooled with diminished focus and conical attentionalperformance.

Finally, and during the latter subtests, a “confederate” (human wizard)presented a collection of hand-crafted alerts, filters and guidance.These emulated the operation of the wearable intervention by offsettingand counterbalancing distracting aural stimuli. To reduce fatigueeffect, these interventions existed in counterbalanced, randomized andmultiple sessions. Alternatively, a combination of alerts, filters andguidance were provisioned to lessen overall length of the experiments,as illustrated in FIG. 3 .

The delivery of isolated and permutated support produced broad measuresof test responses. Mixed method (qualitative and quantitativeevaluations) combined with participants' overt behaviors obtainedthrough audio and video recordings provisioned coding and analysis withsample accuracy synchronization to the systems software. Depending uponsample size and time constraints, this design considers post-hoc videoanalyses (e.g., participant walk-through) in either a structured orliberal form. These examinations may help facilitate recall, precisionand provide further understanding of anxiety and other episodic testingmoments.

Reliability was tested by administering the procedure to a sub-group ofautistic and non-autistic subjects on one occasion over a period of 7separate trials. The I. H. Robertson protocol was used owing to itsheritage and wide acceptance in the scientific community (Robertson etal., 1997).

In the SART procedure, 100 single letters (e.g., A through Z) werepresented visually for up to a 5-minute period. Each letter waspresented for 250-msec, followed by a 900-msec mask. Subjects respondedwith a key press for each letter, except 10 occasions when the letter“X” appeared, where they had to withhold (inhibit) a response. Subjectsused their preferred hand. The target letter was distributed throughoutthe 100 trials in a non-fixed, randomized fashion. The period betweensuccessive letter onset was 1150-msec. Subjects were asked to giveimportance to accuracy first followed by speed in doing the task.

The letters were equally presented in identical fonts (Arial) and size(36 point) corresponding to a height of 12.700008 mm. The mask followingeach digit consisted of a white square with no border or fill coloring.The total area of the mask was dependent upon the user's screen size(e.g., the entire screen would be considered the maskable area). By wayof comparison, a 10-inch diagonal screen would produce a 25 cm diagonalmask for a laptop and a 40 cm diagonal mask for a tablet. Similarly, a15-inch diagonal and 20-inch diagonal screen would produce a 38.10 cmmask and 50.80 cm mask for both laptop and tablet, respectively.

Each session was preceded by a practice period consisting of 15presentations of letters, two of which were targets. Further, aself-assessed state-trait anxiety and state-trait fatigue inventory(STAFI) was conducted prior to and following each of the seven SART/WoZtrials (Spielberger, C. D. (1972). Conceptual and methodological issuesin research on anxiety. Anxiety: Current Trends in Theory and Researchon Anxiety). The fatigue portion in the more commonly used anxiety onlyinventory was combined to measure trait and state anxiety and fatigue.STAFI have been historically used in clinical settings to diagnoseanxiety and to distinguish it from depressive syndromes. The STAFI isappropriate for those who have at least a sixth grade reading level(American Psychological Association. (2011). The State-Trait AnxietyInventory (STAI). American Psychological Association).

Participants selected from five state anxiety items includingillustrations and text that depicted how they were feeling at the momentof query, including: “1—Extremely anxious”, “2—Slightly anxious”,“3—Neither anxious nor calm”, “4—Slightly calm” or “5—Extremely calm”;“I am worried”; “I feel calm”; I feel secure.” Lower scores indicatedgreater anxiety.

Similarly, five fatigue items included illustrations and text thatdepicted feelings at the moment of query, including: “1—Extremelytired”, “2—Slightly tired”, “3—Neither awake nor tired”, “4—Slightlyawake”, or “5—Extremely awake”. Lower scores indicate greater fatigue.

Performance on the SART clearly requires the ability to inhibit orwithhold a response. This is made more difficult when distractors areintroduced into the testing paradigm. Specifically, hand-crafted sonicsof varying amplitude, frequency, time/length, distortion, localization,and phase were introduced to mimic those sounds that might occur inoffice, workplace, education, and scholastic settings.

A total of twenty-eight (28) sound sources were played over a durationof five-minutes and included office industrial, fire alarms, telephoneringing, busy signals and dial tones, classroom lectures, photocopierand telefacsimile operations, footsteps, sneezes, coughs, pencilscribbling, and the like.

Prior to testing, this study accomplished similar testing throughpre-programmed sensing and related interventions. The scripting of sonicstimuli, along with fabricated participant alerts, filters and guidancewere operationalized to give the sensation and response of customizedinterventional support. These smart-system components were pre-defined,and the sensor cause and effect become evaluative to stabilize systemoperation, encourage autonomous testing and synchronized data recording.As in Forbes-Riley, K. and Litman, D. 2011, Designing and evaluating awizarded uncertainty-adaptive spoken dialogue tutoring system, ComputerSpeech & Language 25, 105-126, this study leverages WoZ in place ofmultiple system components; the combination of which presents a fullyintelligent and integrated system. The human wizard is predominantly aconductor/evaluator whose functions and monitoring of programmaticmaterials are unidentified to the participant. Users make selectionsthrough a “dumb” control panel, provisioning their customized alerts,filters and guidance. Importantly, the mechanism advances autonomy byproviding specific functionalities for participant evaluation, whilstostensibly eliminating evaluator influence. In selecting thesecomponents, the following questions are reviewed: What requirementsshould the evaluator meet before conducting a study? How does theevaluator follow the plan, and what measurements will reflect test andsub-test flow? How should control panel component be designed, and howwould this affect its operation? How does the evaluator's personalbehavior affect system operation?

All studies were administered and hosted in the Gorilla IntegratedDevelopment Environment (IDE) and available through most common webbrowsers and appliances (Anwyl-Irving et al., 2019). All audio, videoand related ecological/interoceptive data were presented and collectedin real time via the IDE and evaluator.

Study Variables:

The overarching study was divided into four components including: (i)the PPI study and PTBE described earlier; (ii) the evaluator (includingtasks, self-reports and controls); (iii) the system prototype (anon-wearable sub-system); and (iv) the participants (who were recorded).Study variables are listed in Table 3A, and illustrated in FIG. 18 :

TABLE 3A Variable Type Filter Independent Alert Guidance SystemParticipant Evaluator Interventional combinations: assistance DependentImprovement: focus Improvement: attention Improvement: technologytolerance Reduction: anxiety Reduction: distractibility Reduction:discomfort

SART/Wizard of Oz Protocol Design:

FIG. 6 is a flowchart illustrating the SART/WoZ Protocol used in thisstudy, and includes four higher-order classes that include study aims,variables, assessments, and outcome measures. Study questions,independent and dependent variable, potential assessments/activities andexpected results are also depicted. Based upon this SART/WoZ Protocoldesign, the corresponding class descriptions are listed in Table 3B3:

TABLE 3B Class Descriptors Aims How effective are alerts, filters &guidance in improving intention, reducing both distraction and anxietyin ASC individuals? How tolerable is an Ai/MMLA wearable (even as a WOz)in mitigating sensory issues? Can a single modality system be replicatedsuccessfully across multimodalities? What variables influence each typeof intervention? Variables See Table 3A above Assessments SustainedAttention to Response Task (SART) at baseline no distraction orintervention followed by state anxiety Likert assessment. SustainedAttention to Response Task (SART) at with audio foils and-either: (i)varying interventions followed by state anxiety Likert assessment -or-(ii) combined interventions followed by state anxiety Likert assessment.Sustained Attention to Response Task (SART) return to baseline followedby state anxiety Likert assessment. Outcome Response time measuresAverage test time Percentage correct responses Anxiety/mental healthquotient

Testing Procedures:

Each participant took part in a single experimental session after firstcompleting consent and demographic forms. The session commenced with ashort (1-2 minute) tutorial to ensure that the participant wascomfortable with the proper operation of the testing software, and tointroduce the participant to the importance of staying within range ofthe web camera and pointing devices for proper monitoring of theenvironment and their physiology. After the tutorial, participants wereadvised that the evaluator was available throughout the session to helpmonitor the system and to answer any questions between tests.Participants were not advised of the evaluator's contribution to thetesting (WoZ), that any alerting, filtering or guidance programming waspre-defined prior to the experiment, or that their control of the systempreferences was of a placebo nature.

The WoZ testing (from baseline through multiple interventions and then areturn to baseline) included three phases. Phase I commenced withBaseline I cognitive testing; that is, there were neither distractingcues nor interventions. Phase II introduced accompanying filters, alertsand guidance applied in concert with randomized sonic distractions andtesting. Phase III reintroduced a return to baseline to ensure thatparticipants' recovery and responses were not memorized and thatrandomization effects were properly sustained.

Alerts, Filters and Guidance Structure:

The alerts and guidance of this study protocol utilizes Amazon Polly™, aneural text-to-speech (TTS) cloud service designed to increaseengagement and accessibility across multiple platforms (Neels, B.(2008). Polly. Retrieved Dec. 17, 2020, fromhttps://aws.amazon.com/polly/). Polly's outputs, as listed in Table 4,are cached within the testing system and portends personification of asafe, uncontroversial, newscaster speaking in a style that is tailoredto specific use cases.

TABLE 4 Stimulus Event/Cue Amazon Polly ™ Script Alert: distracting Hi.I sensed a physiological event that interoceptive I wanted to alert youto. Alert: distracting noise Hi. I've sensed a noise that may distractyou, and I wanted to alert you in advance. Alert: distracting visual Hi.I've sensed a visual event that may distract you, and I wanted to alertyou in advance. Filter: distracting noise I am filtering the noise tohelp you re-focus. Guidance: encouragement That's it. I am sensing thatyou're doing quite well at the moment and that you're feeling more incontrol, relaxed and ready to resume your task. Guidance: encouragement2 Good job. Guidance: encouragement 3 Well done. Guidance: encouragement4 Congratulations. Keep up the great effort. Guidance: encouragement 5 Iam proud of you. Guidance: filtering reminder By filtering noise,reminding you to take a deep breath and relax your body, you can moreeasily return to your current task. Guidance: general re-focus Hi. Iwanted to provide you with some friendly guidance to help you re-focusnow. Guidance: general relaxation I want to suggest you take a deepbreath and relax your body position to help you re-focus. Guidance:motivational If you're feeling tired or not reminder motivated to focuson your work, perhaps a few deep breaths, combined with a quick stretchor standing up might be useful. Guidance: re-focus I am providing thisreminder to reminder help you re-focus. Guidance: self-error Oops, Imade a mistake. Sorry . . . I'm still learning what you might finddistracting. The more I work for you, the more accurate I'll become.Thanks for understanding.

A single modality of varying sonic distractions was scheduled fortesting during this study. While both sonic and visual cues can easilybe programmed, for fidelity and deeper understanding, the experimentswere conducted with audio cues only. The stimuli events and cues arelisted in Table 5, along with their accompanying filter name anddescription. The success and efficacy of a prototype wearable device,according to an exemplary embodiment, can be assessed on the basis ofparticipant data collected (both quantitative and qualitative) duringtesting administration of these stimulus event and the participant'sperformance.

TABLE 5 Stimulus Event /Cue Filter type Description Sonic: Spatial SonicPsycho-acoustic ambiguity imager spatial imaging adjustment to enhance,alter or eliminate stereo separation. Sonic: Amplitude Linear Adjustsadaptive distortion multiphase thresholds, makeup Sonic: Amplitudecompressor gain, and finite response over-modulation filters acrossSonic: Amplitude features five user-definable under-modulation bandswith linear phase crossovers for phase distortion-free, multibandcompression. Sonic: frequency Linear phase Up to five bands of bandanomaly (low) equalizer low band and Sonic: frequency broadbandfrequency band anomaly (low- reduction with Sonic: frequency nine phasetypes band anomaly (hi- Sonic: frequency band anomaly (hi) Sonic: timeanomaly C1 Expansion, gaining, (RT60 < 50 Compressor and equalizationSonic: time anomaly sidechaining to (delay < 30 eliminate sonic tailSonic: time anomaly through split-band (delay > 30 dynamics, look Sonic:time anomaly (delay ahead transient processing >50-100 milliseconds) andphase correction. Sonic: phase distortion In phase Real time, dual 1 < x< 30 aligner waveform processing milliseconds for alignment, sidechainto external file, delay control to time compensation, phase shift curveadjustments and correlation recovery.

Protocol Testing Measures:

Participants were instructed to remain in close proximity to theircomputer's web camera and in direct contact with at least one of theirpointing devices (e.g., mouse, trackpad, keyboard) at all times duringthe experiment. Participants were also informed that: measures ofengagement, focus, comfort, productivity, and autonomy would be tested;environmental and physiological/psychophysiological monitoring (e.g.,ecology and interoceptive) would occur during testing; and participanthead sway, pupillary responsivity, GSR, environmental sound and visionwould be collected.

Interventions:

As the study proceeded, participants received combinations of support byway of alerts prior to distraction and/or filtered audio cues (e.g.,distractions that are muted, spatially centered, etc.). Optionally,participants also received post-stimuli guidance to help them return totasks/activities/tests.

Data Collection Method:

This study utilized three data capturing methods direct computerinput/scoring, video analysis and self-reporting. The first isintegrated in the Gorilla application, the second aims to record andmake possible observations of subjects' system interactions, and thethird may reflect the participant's and evaluator's operationexperiences (Goldman, N., Lin, I.-F., Weinstein, M. and Lin, Y.-H. 2003.Evaluating the quality of self-reports of hypertension and diabetes.Journal of Clinical Epidemiology 56, 148-154).

Participants:

The PPI study of verbally abled, autistic (ASC) participants consentedto: (i) focus groups exploring distractibility/attention; and (ii) aLived Experience Attention Anxiety Sensory Survey (LEA²Se) indicatingfirst-person perspectives on sensory, attention and mental healthmeasures. LEA²Se was developed, customized and further modified byfifteen (15) participants who gave autistic voice related to sensory,attentional, and anxiety questions and issues. The LEA²Se was presentedto both autistic and non-autistic participants consented to an OnlineQuestionnaire (autistic: N=187, female=75; non-binary=5; non-autistic:N=174, female=85; non-binary=3) consisting of 103—items for autistic and48—items for non-autistic participants. The questionnaire LEA²Se wasthen utilized as the basis for the WoZ Proof-of-Concept/Trial (POC/T,N=5, 2=ASC, 3=NT, 4/1=F/M) and final trials/experiments. The POC/Tconfirmed adequate systems operation, and translation from userinterfaces to data collection devices and downstream to analysisapplications.

Following the POC/T implementation and prior to the SART/WoZ trials, thePTBE was administered (N=131; 71=ASC, 60=NT; 59=M, 72=F). Eachparticipant was given four discrete tests including the matrix reasoningitem bank (MaRs-IB): a novel, open-access abstract reasoning items foradolescents and adults; the Autism-Spectrum Quotient (AQ): a 50-itemself-report questionnaire for measuring the degree to which an adultwith normal intelligence has the traits associated with the autisticspectrum; and the Adult ADHD Self-Report Scale (ASRS A and ASRS B)Symptom Checklist: a self-reported questionnaire used to assist in thediagnosis of adult Attention Deficit Hyperactivity Disorder (ADHD) andspecifically daily issues relating to cognitive, academic, occupational,social and economic situations.

Based on the PTBE results, a well-matched cohort of SART/WoZparticipants were selected for demographics and test battery results.This yielded a nearly 50/50 balance in neurodifferences betweenexperiment and control groups (N=40; 19=ASC/21=NT;15=M/24=F/1=non-Binary) so that seven randomized, control trials ofpre/post sensory manipulation could take place.

Data analysis examined the use of variables derived from the PPI studyand PTBE described earlier to understand the lived experience ofautistic individuals relating to distractibility, attention, andanxiety. These variables and supporting data were used to predict howparticipants of differing ages and gender might perform on tasksaccompanied by distracting visual, audio, andphysiological/psychophysiological cues. These variables include:Sensitivity Impact; Anxiety Proneness; Distractibility Quotient; VisualDifficulty Quotient; Sound Difficulty Quotient;physiological/psychophysiological (Interoceptive) Difficulty Quotient;and Correlation. Of these, 6 variables, 3 of which are contextuallyrelated to different modalities, were tested in this study. Thedescriptive statistics and correlations for these variables are listedin Table 6:

TABLE 6 Variable Median IQR AP DQ SDQ VDQ PDQ Sensitivity Impact (SI)2.50 1.13 .872 .713 −0.50 −.750 −.622 Anxiety Proneness (AP) 2.44 0.74.748 −0.86 −.753 −.619 Distractibility Quotient 2.56 1.33 −.241 −.822−.786 Visual Difficulty Quotient 5.50 3.50 .255 .217 Sound DifficultyQuotient 4.50 2.00 .866 Physiological Difficulty 5.67 5.00

Stepwise Regression:

Dummy variables were created for both age and gender (i.e., the onlydemographic factors that were not correlated), and were combined withsensitivity, anxiety and distractibility variables (SI, AP and DQ)embedded within a stepwise regression analysis to predict scores insound, visual and physiological/psychophysiological/interoceptivemodalities. The model(s) with the highest R2/significance are reportedin Table 7:

TABLE 7 Sound  Predictors: Distractibility Quotient, Gender, SensitivityImpact  Model Significance: F(3, 185) = 12.98, p < .001    DQ: t = −2.82p < .001 β = −.476    Gender: t = 3.94 p < .001 β = .272    SI: t = 2.32p < .021 β = .233  R = .419  R² = 17.5% Visual  Predictors:Distractibility Quotient, Sensitivity Impact  Model Significance: F(2,185) = 241.46, p < .001    DQ: t = −9.40 p < .001 β = −.528    SI: t =−6.89 p < .001 β = −.387  R = .85  R² = 72.4% Physiological  Predictors:Distractibility Quotient  Model Significance: F(1, 185) = 329.43, p <.001    DQ: t = −18.15 p < .001 β = −.800  R = .80  R² = 64%

Standard Regression

One categorical variable regressed (i.e., either age or gender,depending on which was significant in the previously conducted ANOVAs)with a continuous variable (either Sensitivity Impact [SI],Distractibility Quotient [IDQ] or Anxiety Proneness [AP], depending onwhich had the highest correlation) onto the three modalities (e.g.,sound, visual and physiological/psychophysiological), all of which serveas dependent variables in this study. Standard regression values areshown in Table 8:

TABLE 8 Sound  More correlated to DQ than SI  Gender + DQ = R² of 15.1% Age is not correlated (ANOVA wasn't significant)  Anxiety proneness hashigher correlation, but not significant with gender Visual  Age + SI +DQ = R² of 73.5% (but DQ and SI are correlated)  Age + SI = R² of 64.1% Gender not significant in ANOVA Physiological  Age + DQ − R² = 65.7% Gender not significant

PTBE Results/Data Analysis:

For the initial run of SART/WoZ participants (N=37, mean age 25.70,S.D.=7.442), their mean PTBE scores ranked as follows: MaRs-IB=62.20%and 18.64; AQ=24.51 and 12.66; ASRS—1=3.19 and 1.66; and ASRS—2=5.51 and3.30). Independent samples tests for all PTBE results yielded MaRs-IB of(F=0.166, t=−0.295, df=35 and Sig. 2-tailed=0.769); AQ of (F=0.046,t=4.494, df=35 and Sig. 2-tailed=0.000), ASRS—1 of (F=0.281, t=2.757,df=35 and Sig. 2-tailed=0.009); and ASRS—2 of (F=0.596, t=2.749, df=35and Sig. 2-tailed=0.009). Demographic independent sample tests wereinsignificant across age, gender, handedness, education, employment,income, status, children, home, and location.

For PTBE participants (N=131; autistic=71, non-autistic=60, and thosewho were tapped for the SART/WoZ) an ANOVA comparing autistic versusnon-autistic participants utilizing Levene's test showed that thevariance was significant in all scores such that: MaRs-IB scores were(F(1, 129)=4.143, p=0.044), AQ scores were (F(1, 129)=81.090, p<0.001),ASRS—1 scores were (F(1, 129)=4.832, p=0.030), and ASRS—2 scores were(F(1, 129)=8.075, p=0.005).

Similarly, and for the identical sample, an ANOVA comparingparticipants' genders utilizing Levene's test showed that the variancewas insignificant in MaRs-IB scores were (F(1, 129)=0.143, p=0.705), AQscores were (F(1, 129)=0.008, p<0.930), ASRS—1 scores were (F(1,129)=0.973, p=0.326), and ASRS—2 scores were (F(1, 129)=0.018, p=0.893).Cohort and group score averages are listed in Table 9, and shown in FIG.7 :

TABLE 9 Cohort ASC NT MaRs-1B N = 131 N = 71 N = 60 Average  67.006%70.567%  62.792% Maximum 100.000% 100.00%  92.308% Minimum  18.667%18.667%  20.000% AQ (50) N = 131 N = 71 N = 60 Average 25.832 31.36619.283 Maximum 45.000 45.000 41.000 Minimum 3.000 9.000 3.000 ASRS(Everyday Distractibility/Attention) N = 131 N = 71 N = 60 Score A Ave2.954 3.211 2.650 Score B Ave 5.275 5.915 4.517 Score A Max 6.000 6.0006.000 Score A Min 0.000 0.000 0.000 Score B Max 12.000 12.000 11.000Score B Min 0.000 0.000 0.000

SART/WoZ Results/Data Analysis:

Errors of Commission (EOC) Performance

For the entirety of the SART study, and from baseline-to-baselineretest, the performance of the cohort (N=40) consisting of autistic/ASC(N=19) and neurotypical/non-autistic/NT participants (N=21) exhibited animprovement in performance. That is, there was an average reduction inSustained Attention to Response Task (SART) errors equaling 7.46% forall inhibition measures across the entire cohort (FIG. 8A). The samecohort averaged an improvement of 14.50% (again, in error reduction) fora different interval; that is, from the onset of distraction cues toalert intervention. Finally, similar improvements occurred fromdistraction cues to a differing intervention (this time 10.27%) forcombinatorial assistance (e.g., alerts, filters, and guidance; FIG. 8B).Regardless of the intervention, improvements were markedly prevalent forthe entire cohort. Remarkably, and even after interventional cessation,a long-lasting improvement of 17.52% reduction in errors persisted amongthe cohort once the four technological assists were suspended (FIG. 8C).This resulted in a specific and average improvement of 1.45 fewer errorsper participant, regardless of their diagnoses (group membership). Ineach measure, the improvement trend line was well correlated (baselineto baseline, intervention only, and intervention removal).

Errors of Commission Response Times (EOC-RT)

In general, response times increased for the entire cohort whenparticipants experienced exposure to interventional assistance.Regardless of counterbalancing trials and their internal randomization,the cohort's improved accuracy occurred because of increased/slowing RT(e.g., 21.74% increase from baseline to alert intervention). Note that aslowing in RT is actually a desired effect from the intervention, as isexplained in detail below. It is worth mentioning that unlike EOC, therewas insignificant last effect of RT (resulting in 10.69% fasterresponses once interventions ceased).

In comparison, autistic response times were shorter (faster) thanneurotypical controls. This can be due to various factorsdifferentiating neurodiverse responsivity-including, but not limited to,greater neural processing, differences in genetic makeup affectingsensory reactivity, and superior activity in the visual cortex(Schallmo, M.-P., & Murray, S. (2016). People with Autism May See MotionFaster. 19). For errors of commission, autistic participants experienceda RT increase of 19.39% (i.e., a desired slowing from onset ofdistraction to guidance intervention) while neurotypical counterpartsproduced an undesirable decreased in RT (speeding up) of nearly onepercent (−0.74%) for the same period. Reaction timing's effect onaccuracy saw an improvement of for 8.67% ASC participants and a 1.27%increase for neurotypical (NT) participants. These results are shown inFIGS. 9A to 9C, which are graphical representations of EOC as it relatesto Response Time (RT) of the full cohort of participants in the SART/WoZstudy described herein. FIG. 9A shows the EOC vs RT from startingbaseline to final baseline, FIG. 9B shows the EOC vs RT interventioneffect, and FIG. 9C shows the lasting effect of EOC vs RT.

Note that a slowing of reaction time portends to greater mindfulness,which can be defined as a participant's awareness of their internalfeelings and a subsequent ability to maintain awareness withoutevaluation or judgement (e.g., defined as an outcome). Therapeuticallyspeaking, the wearable device described herein cultivates mindfulnessvis a vis bespoke intervention (assistive technology). This helps toshift and shape a participant's wandering mind and their awareness.Essentially, the participants in this study become more aware,productive, and comfortable through alerts, filters, and guidance whenexposed to sensory interruptions during a Sustained Attention toResponse Task (SART). Over time, participants become more attentive,less sensitive, less anxious, and less fatigued.

Realizing that slowing RT is not an unfavorable outcome, but rather adesirable one, the data suggests that NT participants who previouslyexperience decreasing RTs (speeding up that produce smaller performancegains) can be further improved by utilizing alerts rather than guidance.This results in NTs experiencing a desirable increase in RT (slowingdown). Specifically, and for the period of onset of distraction toalert, both ASC and NT slow their RTs. As a result, EOC improved amongboth autistic and non-autistic participants by 26.01% and 17.59%,respectively. For the same period, ASC and NT participants improvedtheir RTs by 2. 94% and 1.9%, respectively.

While neurotypical gains in accuracy appear small (i.e., from 1.27% to1.9%), this represents a 50% (49.60%) improvement. Thus, slowingresponse times, resulting from custom interventional assists, createbetter performance outcomes. Autistic performance also improved by 200%(e.g., 8.67% to 26.01% accuracy). These results are shown in Tables 10Aand 10B:

TABLE 10A Slower (ASC) vs. Faster (NT) Response Times ffect on AccuracyEOC Response (with guidance Times Accuracy intervention) IncreaseIncrease ASC 19.39% 8.67% NT (−0.74%) 1.27%

TABLE 10B Slower (ASC and NT) Response Times Effect on Accuracy EOCResponse (with alert Times Accuracy intervention) Increase Increase ASC 2.94% 26.01% NT 17.59%   1.9%

The divergence between speeding and slowing RTs (and its effect onexperimental and control groups) is not accidental. Evidence of reverseRT effect on accuracy; that is, faster RT produces greater accuracy, issupported after repeated interventional assists are removed and thenmeasured. The long-lasting effect of fewer errors (e.g., 17.92% and17.09% reductions for ASC and NT, respectively) occurred even whenresponse times lessened (e.g., 21.48% and 2.688% faster RTs for ASC andNT, respectively). These are small, but meaningful reductions amountingto average gains of 19.095 ms for autistic and 2.913 ms for non-autisticparticipants. Still, lasting intensification in performance occurred,despite diminishing response times.

The trend or tendencies of response times provide interestingconsiderations. Specifically, and for the entirety of the seven trials,autistic and non-autistic RTs diverge. Autistic RTs increased from 88.89ms to 69.80 ms for baseline to baseline-retest (a speeding up of 29.78ms over the period). In comparison, neurotypical RTs decreased from82.59 ms to 105.46 ms, or a slowing down of 22.86 ms. This renders a52.64 ms gap between the experimental and control group that ismodulated once interventions are applied.

Explicitly, RTs increase (slow down) 17.72 ms for both ASC (i.e., fromdistraction onset to guidance intervention) and for NT (i.e., by 19.06ms for distraction onset to alert interventions). These represent themaximum increases in RT for both groups and are non-contrasting (i.e.,again, both slow down). Equally significant is RTs lasting effect; thatis, neither autistic nor non-autistic participants benefit from aslowing RT once the intervention is removed. Both ASC and NT groupsspeed up their responses by 19.10 ms and 2.91 ms, respectively (eventhough there is positive lasting performance by way of fewer errors).These results are shown in FIGS. 10A to 10C.

Additionally, as shown in FIGS. 11A to 11C, autistic response times aretypically faster than neurotypical participants for the same tasks andinterventions. Similarly, while reduced errors (improved performance)occurs across both groups, autistic participants exhibit greatervariability in improvement, while neurotypical participants producefewer errors overall. The only exception we see is for combinedinterventions (e.g., alerts, filters, and guidance) where both NT andASC are equivalent a lessoning to 7.4 errors each.

In summary, as a cohort and within subjects/groups, performance increase(e.g., fewer errors) stems from interventional support applied andmeasured from the onset of a sensory distraction to assistivetechnology. A 14.5% improvement for the entire cohort results with alertintervention. Modulating the intervention (i.e., applying filters andguidance to the alerts) results in variable improvement as well. Thecohort improved 10.27% in performance from a combination of theseinterventions. Autistic participants revealed greater performance(26.01% fewer errors) with alert intervention, while non-autisticenjoyed a 5.7% improvement through filter interventions. Lasting effectson performance improvement among the entire cohort (17.52%) andindividual groups (ASC 17.92% and NT 17.09%) continued well afterinterventions were suspended.

Reaction times increase when participants receive assistivetechnologies. By slowing down, participants enable and experiencegreater mindfulness which yields increased performance. From baseline toalert interventions, the cohort averaged a 21.74% slowing in RT and fromthe onset of distraction to alerts, the slowing was 11.35%. Whenremoving interventions of any kind and from baseline tobaseline-retesting, RT sped up (decreased) by 10.69%. From anexperimental to control group comparison, autistic and non-autisticparticipants diverge with RTs decreasing for ASC participants andincreasing for NT subjects. Nonetheless, both groups benefit underinterventional measures with increased performance, while neither groupbenefits from any lasting effect on RTs once assistive technologies areremoved.

Errors of Omission (EOO) Performance:

For the entirety of the SART study, and in addition to studying cohortperformance (N=40, ASC=19, NT=21) on Errors of Commission (e.g., notinhibiting a response when instructed to do so), Errors of Omission werealso analyzed. EOO refer to not responding properly for any stimuluswhen inhibition is not warranted or instructed. For the same testingperiod and from baseline-to-baseline retest, EOO increased 49.16%. Thismeans that there was an average increase in Sustained Attention toResponse Task (SART) measuring, on average, 2.2 errors per participant(FIG. 12A).

While not a desirable result, the cohort averaged an improvement wheninterventions were present. Specifically, and from the onset ofdistraction cues to alert interventions there were 47.60% fewer EOO.Similar improvements occurred from distraction cues to combinatorialinterventions (though this time a smaller improvement of 23.12%), asseen in FIG. 12B.

Remarkably, a long-lasting improvement of 10.10% reduction in EOOpersisted among the cohort once the four technological assists weresuspended (FIG. 11C). This was calculated by measuring the errorpercentage increase from baseline to baseline-retest and thensubtracting the error improvement measured from distraction throughbaseline-retest. For each participant, this corresponded to an averageof 1.86 fewer errors for each, regardless of their diagnoses/groupmembership. In each measure, the improvement trend line was wellcorrelated (baseline to baseline, intervention only, and lastingeffect).

Errors of Omission Response Times (EOO-RT)

In general, response times for the entire cohort increased whenparticipants experienced exposure to interventional assistance, but notfrom baseline to baseline-retest (which remained relatively flat at−0.20%; FIGS. 13A to 13C). Regardless of counterbalancing trials andtheir internal randomization, the cohort's initial reduced andeventually improved EOO accuracy occurred because of increased/slowingRT (e.g., 5.16% increase from Baseline to Filter intervention). Again,this slowing in RT is a desired effect from the intervention, as isexplained earlier in the EOC section. It is worth mentioning that unlikeErrors of Commission, there was insignificant lasting effect of RT(resulting in 3.48% faster responses once interventions ceased).

EOO response times resembled EOC for autistic participants; in that,both were faster than neurotypical controls, due in part to previouslymentioned neuronal processing and responsivity. Thus, autisticparticipants experienced an RT increase of 9.28% (i.e., a desiredslowing from onset of distraction to filters intervention), whileneurotypical counterparts produced an undesirable decrease in RT(speeding up) of nearly one percent (4.44%) for the identicalintervention.

In comparison to EOC RT, neurotypical results are slightly faster(poorer), for e.g., EOC vs. EOO yielded 126.94 ms to 122.05 ms.Considerably more favorable results occurred for ASC participants (e.g.,EOC vs. EOO yielded 91.02 ms to 114.19 ms). Contrastingly, RTs' lastingeffect on performance observed as a reduction (speeding up) for ASC(7.25%) and a relative flattening, albeit a slight reduction (1.2%) forNT participants. These results are shown in FIGS. 14A to 14C.

RTs also effect Errors of Omission, when comparing autistic andnon-autistic groups. There is a lessening of EOO (though these stillproduce inaccuracies) among neurodiverse participants (−15.12%).Similarly, an increase in accuracy (less EGOs) are exhibited amongneurotypical participants. Unsurprisingly, faster RT (4.44% in the caseof NT participants from distraction to filter) did, in fact, create moreerrors (15.09%). As would also be expected, slower RTs among autisticparticipants (9.28%) resulted in fewer EGOs (15.12%). Curiously, bothgroups responded oppositely to similar intervention (by way of RTs), andby equal and opposite magnitudes in accuracy with NTs (not ASCparticipants) experiencing greater errors.

This seem implausible; but, when regarding the entirety of data (i.e.,baseline to baseline-retest) for study participants, the RT and EOOcurves are indeed inversely proportional. Longer RT produces, asexpected, fewer EOO. Less correlated, however, are neurotypical RT.Higher (desirable) NT RTs produce fewer errors (also desired) underfilter intervention. However, greater RTs with guidance produce moreEGOs (undesirable). Thus, and depending upon the group, longer RT have adiminishing return on accuracy. Where Errors of Commission bettercorrelate with response time variance, Errors of Omission do notcorrelate well to RT.

Even though there is an improvement among autistic participants bearinggreater accuracy, this occurs through an unusual lessening of EOOresponse times. Greater accuracy (29.07%) and improvement fromdistraction onset to guidance intervention occurs with less RT slowing.Additional deceleration (9.28%) from distraction to filtering producesmore inaccuracies (15.12%). Non-autistic participants accuracy performsas expected; that is, an increase from −4.4% to −1.39% (e.g., a slowingof RT) produces an 8.5% increase in accuracy (15.09% to 23.59%). Theseresults are shown in Tables 11A and 11B:

TABLE 11A Slower (ASC) vs. Faster (NT) Response Times Effect on AccuracyEOO (with Filter intervention for Response both ASC and Times AccuracyNT) Increase Increase ASC 9.28% (−15.12)% NT (−4.44%) 15.09%

TABLE 11B Slower (ASC and NT) Response Times Effect on Accuracy EOO(with Response Guidance Times Accuracy intervention) Increase IncreaseASC 1.24% 29.07% NT (−1.39)% 23.59%

As presented, the correlation between speeding and slowing RTs (and itseffect on the accuracy of experimental and control groups) is notaccidental for EOC. Contrastingly, there is a divergence in EOO scores.Evidence of reverse RT effect on accuracy does occur; that is, faster RTdon't always produce greater inaccuracy.

In the previous table, autistic response times that increased 9.28%resulted in a negative accuracy increase (e.g., inaccuracy). However, aspeeding up (or reduction of response times to 1.24%) produced greateraccuracy (29.07%). This unexpected autistic divergence is not exhibitedin neurotypical EOO. The increase in speed (−4.44%) produces a loweraccuracy of 15.09% errors, while a slowing to 1.39% (an increase inspeed) produces expected and higher accuracy (23.59%). These results areshown in FIGS. 15A to 15C.

The long-lasting effect of fewer errors is absent (e.g., both ASC and NTsee increased EOC by 51.16% and 29.25%, respectively) while RTaccelerated 7.58 ms for autistic and 1.53 ms for non-autisticparticipants. While increased errors are expected when RT is faster,this is in direct contrast to EOC lasting effect. Put simply, just as RTerrors of omission does correlate well to EOC, the lasting effectexhibited on EOC performance/accuracy does not hold true for EOO.

Like EOC RT, EOO responses for autistic participants response remainboth narrow and consistently faster than their more variable andneurotypical counterparts. And while reduced errors of omission(improved performance) occurs across autistic participants, there isless variability in this improvement, while neurotypical participantsdon't necessarily produce fewer errors. This is in stark contrast to EOCdata. These results are shown in FIGS. 15A to 15C.

In summary, unlike the identical cohort and within subjects/groups thatexperienced a performance increase (e.g., fewer errors of commission),errors of omission were not equally reduced from the onset of a sensorydistraction to assistive technology. While a 15.16% reduction in EOOoccurred for autistic participants (from distraction onset to filterintervention), no reduction occurred for any interventional applicationamong neurotypical participants.

The combined effect on the entire cohort also proved unremarkable froman EOO improvement standpoint. Again, only the autistic (experimental)group experienced benefits. It's worth mentioning that modulating theintervention to other forms (e.g., alters, guidance and combinations)had no appreciable improvement for neurodiverse participants. Onlyfilter intervention proved assistive. Lasting effects of performanceimprovement eschewed the entire cohort as there were 10.10% more errorsof omission. The same remained consistent for experimental and controlgroups once interventions were suspended (e.g., 51.16% and 29.25%increase in EOO for ASC and NT, respectively).

Reaction times increased for the entire cohort when assistivetechnologies were invoked. By slowing down, participants enable andexperience greater mindfulness which yields increased performance. Frombaseline to filter interventions, ASC participants averaged a 9.28%slowing in RT and from the onset of distraction to alerts, the slowingwas 1.535%. Neurotypical participants undesirably sped up 4.44% underthe same filter interventions but managed to slow down for both guidance(2.61%) and combination interventions (4.58%).

When removing entire cohort interventions of any kind and from baselineto baseline-retesting, RT sped up (decreased) by 3.48%. Thus, there wasno significant lasting effect on EOO RT.

Similarly, and for both experimental to control groups, autistic andnon-autistic participants experienced decreasing RTs and no significantlasting effect on EOO. ASC RTs sped up 7.25% whilst NT RTs sped up1.20%.

As alluded to above, the PPI study examined issues and connections amongthree variables: sensory (sensitivity), mental health (anxiety andfatigue), and distractibility (attention). The PPI study was used todevelop a sensitivity mental health distractibility model, depicted byFIG. 26 , designating how anxiety and fatigue can mediate sensorysensitivity and distractibility, within both autistic and non-autisticdiagnostic groups. The model of FIG. 26 links sensory cues (e.g.,labeled #1 that includes an individual's hyper, hypo- andsensory-seeking characteristics) to mental health mediators (e.g.,labeled #2 that describes an individual's anxiety and/or fatigue) todistractibility (e.g., labeled #3 that explains an individual's capacityto focus/maintain attention). From an ordering standpoint, the modelextends sensory cues through mental health characteristics that canfurther modulate an individual's attentional reactivity, versus astraight line leading from cue to distractibility alone. Whilesensitivity has been previously hypothesized to disrupt top-down andbottom-up attention, the model of FIG. 26 embodies a new and lateralrelationship more fully depicting an autistic individual's sensitivityand attention processing.

FIG. 27 is a flowchart depicting the design/method of the PPI studydiscussed above. The PPI study was implemented in two parts. The firstphase consisted of five autistic-only (14 adults, 18-54-year-old) onlinefocus groups to better understand daily experiences relating to sensorysensitivity (FIG. 27 , Item #2). The focus groups examined how sensorysensitivity impacted both attention and mental health across threethemes: (i) lived experience in adult contexts of higher educationinstitutions, employment, and social venues; (ii) technology tolerance,and digital mediations that can help autistic individuals in adversesensory environments; and (iii) language that was relevant, easy tounderstand, and autism-friendly for the Phase 2 questionnaire with alarger group of participants.

As depicted by FIG. 27 , Item #6 and #7, the second phase involved anonline questionnaire of both autistic (N=187) and non-autisticparticipants (N=174). Both groups answered questions about their ownsensory-sensitivity (visual, auditory, and physiological), focus(distractibility), and mental health (anxiety and fatigue), and—in thecase of the autistic-only group—questions about their interest, personaldesire, and tolerance of assistive technologies. The questionnairedesign provided for diversity in lived experiences by allowingopportunities for open-ended responses, in addition to multiple choicequestions.

Table 12 shows the demographics of the participants in the first phaseof the PPI study.

TABLE 12 Feature Autistic (N = 14) Sex Female 7 Male 6 Non- 1 binary Age18-20 7 21-29 2 31-39 5 Location UK 8 US 6

Participants were recruited through opportunity sampling using bothuniversity databases in the UK and US, combined with worldwide socialmedia. All individuals identified themselves as autistic and/orindicated they possessed a form diagnosis. Participants ages 17-38 years(N=14, female=7; non-binary=1) participated in one of five scheduledfocus groups, each with a minimum of two and a maximum of four people.Each hour-long online, focus group utilized on screen presentations toguide discussions, which helped delve into participants' livedexperience about sensory sensitivity, technology, attention, and mentalhealth. A collection of more than 100 questions was summarized andcombined into seventeen items and arranged across six overarching topics(FIG. 27 , Item #1) depicted by Table 13.

TABLE 13 Themes and questions What distracts you more: auditory orvisual cues? If you think about sensitivity and its impact on you, howhas this affected your performance on the job, and your autonomy, oryour quality of life? Theme 1: Sensitivity/Impact Q.1. What type ofsensitivity, distraction, anxiety or focusing issues have youexperienced lately or throughout your life? Q.2. How have these affectedyour performance, autonomy, quality of life, etc.? Theme 2:Anxiety/Fatigue Q.3. Are you likely to become anxious/fatigued if youexperience sensitive stimuli like sounds, visuals, etc.? Q.4. How doesthis affect your ability to focus, complete tasks, enjoy hobbies, affectyou physically, etc? Theme 3: Over-sensitivity Q.5. Would you describeyourself as over-sensitive, under-sensitive or sensory seeking when itcomes to sounds, visuals, etc.? Q.6. How are you at dealing withinterruptions, staying focussed, or working in distracting environments?Q.7. What opinions do you have about distraction, anxiety, fatigue, andattention? Theme 4: Technology and Devices Q.8. How often do you useSmart Devices like iWatch, FitBit and Smart Clothing? Q.9. What are yourthoughts about Smart Devices like Body Cameras, Bone ConductionHeadphones and Mobile Phones? Q.10. Do you ever use smart glasses orother wearables? Q.11. Do you enjoy technology? Theme 5: TechnologyTolerance Q.12. Would you enjoy learning how wearable devices may helpin challenging places with focus, distraction, and anxiety? Q.13. Howmight a device help if it alerted you to customized stimuli that affectsjust you? Q.14. Would you be more likely to use a wearable provided thatonly you controlled when/if you were assisted? Theme 6: Experience Q.15.How would you describe your sensitivity to distracting sounds anddistracting visuals? Q.16. Do you enjoy distracting places and seek themout? Q.17. Would you describe yourself as sensitive or unaffected bydistracting stimuli?

These questions corresponded to screen presentations and were read aloudby the researcher to prompt participant responses. As participantsreplied, participants were reminded to express their thoughts andfeelings about how technologies can be used to accommodate them in theirdaily living and assist with activities (FIG. 27 , Item #3). Forexample, when asking Q.6.: about how one “deals with interruptions,staying focused, or working in distracting environments?”, theresearcher might follow a participant's response by probing more deeplyby inquiring: “what type of assistive device might help you resist orcontend with the interruptions you just described at your job; or howwould the technology help you succeed?”.

Participants were encouraged to describe any concerns they had about thewording of questions (e.g., ambiguity, terminology that was difficult tounderstand, and the relevancy of topics under consideration) and tofreely provide alternatively wording using autistic-friendly language(FIG. 27 , Item #4). For example, a participant identified Q.3. asunclear because it was too broad; they suggested rephrasing it to aYes/No question: “Soft sounds make me nervous and tired”. Uponconcluding the final meeting, all focus group audio/video recordingswere transcribed and imported into a qualitative data analysis (QDA)computer software for further analysis. These transcripts werecoded—first by arranging responses into the six previous topics, then byparticipant valence, and finally by any suggested, alternative wordings.The QDA software was programmed to output a list of response words thatwere mentioned ten or more times, shown by Table 14 below.

TABLE 14 (Commonly used participant expressions in response to focusgroup questions (occurring at least 10 or more times). Chapter 1 Topics(expressions not bolded) Mixed Negative Neutral Positive TotalSensitivity/Impact Other Venues 3 10 1 9 23 School 5 13 1 4 23 Social 1934 2 20 75 Work 1 18 4 11 34 Anxiety/Fatigue Anxiety 9 36 2 9 56Insomnia 2 7 1 2 12 Over-sensitivity Distraction 2 12 0 0 14 Focus 4 111 2 18 Interruptions 1 20 1 0 22 Job performance 0 10 1 1 12 Networkingand Socialization 1 15 0 1 17 Sensitivity and aging 2 8 0 2 12Technology and Devices Light hyposensitivity 0 4 0 6 10 Lightsensitivity 1 15 1 4 21 Sensory seeking 10 11 3 13 37 Sound sensitivity9 42 2 8 61 Technology Tolerance Non-tolerance 2 10 0 2 14 Tolerance 4 81 17 30 Experience Alert 2 5 1 26 34 Filter 0 2 0 10 12 Guidance 0 4 020 24 Device interest 1 2 1 18 22

The twenty-two response words were arranged along with their counts intothe six, original topics, which permitted examining the existence,prominence, and word relation to sensory, attention, technology, andmental health issues.

As depicted by Table 14, prominent words were split into one of fourvalence categories, which illustrated how participants expressed concernof these issues within the thematic context and of the questions askedof them. For example, a participant may have described their ‘positive’interest in using a technology to help overcome anxiety, or theyexpressed neither ‘positive’ nor ‘negative’ feelings (i.e., ‘neutral’)about light sensitivity, etc. Higher valence counts (totaling 10 ormore) were judged to be more important than lower counts. These countswere used to develop phase 2 PPI questions, further discussed below.

Suggested modifications to wording of questions was studied, and the QDAcomputer software was used to output a list of alternative wordsdepicted by Table 15.

TABLE 15 (Autistic-friendly, alternative words used to create Phase 2questionnaire). Alternative Weighted Word Count % people 102 2.34%things 101 2.32% distractions 87 2.00% help 65 1.49% lights 65 1.49%anxiety 58 1.33% noises 57 1.31% focus 48 1.10% places 47 1.08%sensitive 46 1.06% technology 43 0.99% time 43 0.99% feel 40 0.92%anxious 39 0.90% loud 39 0.90% bother 35 0.80% music 33 0.76% hear 280.64% sensory 28 0.64% environments 27 0.62% device 27 0.62%interruptions 27 0.62% sleeping 25 0.57% happen 24 0.55% talk 24 0.55%glasses 22 0.51% understand 22 0.51% heard 22 0.51% control 21 0.48%look 21 0.48% affect 19 0.44% alert 18 0.41% seeking 18 0.41% cause 170.39% difficult 17 0.39% room 17 0.39% visual 17 0.39% social 17 0.39%situation 16 0.37% bright 16 0.37% annoying 15 0.34% watches 15 0.34%body 15 0.34% different 15 0.34% wearable 15 0.34% filter 14 0.32%

Forty-six items ranked by word count and weighting (i.e., individualword count divided by the total count of all alternatives) were sorted.Table 15 was used to reword 79 of the original 103 questions byreplacing troublesome words with alternatives suggested by participants.Question modifications were also based upon a visualization report (FIG.27 , Item #5; and FIG. 28 ).

At the conclusion of the final focus group, a word cloud depictingautistic-voiced expressions was created using data from Table 15. Theword cloud is depicted by FIG. 28 . The participants were sent the wordcloud to gauge their satisfaction with alternative words and receivedunanimous approval (both written email reply and/or follow-on internetchat). This concurrence was used to help select which questions would bere-voiced to reflect autistic alternative wording. For example,participants mentioned that:

-   -   Centrally located, larger, and darker colored word cloud terms        would be appropriate to compose phase 2 questions using        alternative like “things”, “people”, and “distractions”.    -   Items just outside the cloud's midpoint would be suitable when        questioning sensory cues and reactivity, including “needing        help”, “feeling anxious”, and “being bothered”.    -   Words near the cloud's edge were appropriate to compose        questions about technology, including “wearable”, “coaching”,        and “assisting”. These were supportive when questioning outcomes        about “comfort[able]”, “participat[ion]”, and “performance”.

Table 16 shows the demographics, including gender, age, and educationlevel, of the participants in the second phase of the PPI study.

TABLE 16 Non- Autistic autistic (N = 187) (N = 174) Gender Male 107 86Female 75 85 Non-binary 3 3 Age 18-20 24 22 21-29 42 37 31-39 111 10841-49 10 7 Education level University 33 53 without degree Universitywith 132 87 degree Graduate school 22 34

Participants were recruited similarly to focus group individuals usinguniversity websites and social media. Non-autistic individuals wererecruited and well-matched to ensure compatibility with theirneurodiverse counterparts. Both group's eligibility criteria includedages between 18-49 and English-language proficiency. Non-autisticparticipants were specifically required not to self-identify or possessan autism diagnosis. After eliminations, a total sample of N=361individuals were analyzed (i.e., 187 autistic and 174 non-autisticparticipants).

The online questionnaire was designed to expand the initial focus groupinquiry beyond word usage and their alternations to data collection thatdescribed sensory patterns, technology experiences, and desire foraccommodations—all described using genuine, clear, and relatablelanguage by a larger sample of respondents who might share experiencesidentified by the smaller group of original participants.

A majority of phase two questions were derived from various sourcesincluding the UCL Student Mental Health Survey (SENSE; McCloud et al.,2019), the Stanislaus State Concentration Questionnaire (CQ, 2019), theCogniFit Online Cognitive Assessment Battery for Concentration (CAB-AT,2018), the Cognifit Cognitive Assessment for ADHD Research (CAB-ADHD,2018), and those from semi-structured interviews developed by Ashburnerand colleagues (2013). These questions were supplemented and/orfine-tuned by the researchers.

Using alternative words, 79 of the original 103 questions that weredeployed online to autistic only participants (FIG. 27 , Item #6) weremodified. A second questionnaire was deployed using the same wording butfor non-autistic participants (FIG. 27 , Item #7). 55 of the original103 questions that focused on technology usage and tolerance wereexcluded from the non-autistic version, including, for example: “Howoften do you use a smart watch?”; “I think I would enjoy owning awearable device that knows my preferences about what I finddistracting”; and “I would be interested in learning how a wearabledevice might help me in environmentally noisy situations”, as it wasthought that such questions would be less helpful when comparingsensitivity, attention, anxiety, and fatigue characteristics betweendiagnostic groups. Their removal yielded a 48-item non-autisticquestionnaire.

Online demographic responses were imported using QDA software, andexamined as follows:

-   -   Demographic questions. Education was used as proxy to gauge        intelligence levels, to match groups on ability, in deference to        autistic heterogeneity (Mottron, 2004), and to avoid collecting        intelligence quotient (IQ) data.    -   Sensory sensitivity variables. Inquiry spanned modalities using        three input variables: visual, auditory, and physiological.        Unless otherwise specified, a 5-point Likert scale was utilized        with responses ranging from strongly disagree (1 point) to        strongly agree (5 points). Mean scores were computed and ranged        from 1-5 with higher scores indicating greater sensitivity.        Sample queries included “At work or at school, I have        experienced sensitivity, distractibility or anxiety because of        visual reasons (for example: lighting, movements, colours,        etc.)” and “I am sensitive to vibrating wearables (for example,        a mobile phone, FitBit, iWatch or another notifying device)”.    -   Visual Sensitivity Variable (VSV). Nine questions—derived from        Stanislaus State's Concentration Questionnaire (CQ, 2019) and a        Cognitive Assessment Battery for Concentration (CAB-AT; Cognifit        2018)—defined motion, light, and other cues having an adverse        bearing on one's activities. Sample queries included “I am        easily distracted or sensitive to certain environmental        sights/visions” and “I avoid visually stimulating environments”.    -   Auditory Sensitivity Variable (ASV). Seven questions—derived        from the Stanislaus State Concentration Questionnaire (CQ,        2019), the Cognitive Assessment Battery for ADHD Research        (CAB-ADHD; Cognifit, 2018), and semi-structured interviews        (Ashburner et al., 2013)—described loud, startling, and other        cues unfavorably affecting individuals. Examples questions        included “I am easily distracted or sensitive to certain        environmental sounds (for example, noises, loudness, pitches,        conversations)” and “I would describe sounds like humming of        lights or refrigerators, fans, heaters or clocks ticking as        distracting”. Four questions were reverse scored reflecting        higher scores that indicating greater sensitivity.    -   Physiological Sensitivity Variable (PSV). Six questions—derived        from the Stanislaus State Concentration Questionnaire (CQ,        2019), and the Cognitive Assessment Battery for ADHD Research        (CAB-ADHD; Cognifit, 2018)—related to exocentric effects and        egocentric sensations of attention and anxiety. Sample queries        include “I do not like being touched” and “I am easily        distracted or sensitive to certain physiological feelings of        thoughts (for example anxiousness, racing thoughts, ringing in        my ears)”.    -   Anxiety Variable (AV). 16 questions—derived from UCL's Student        Mental Health Survey (SENSE; McCloud et al., 2019), interviews        (Ashburner et al., 2013), a Cognitive Assessment Battery for        Concentration (CAB-AT; Cognifit 2018)—described how sounds,        sights, and sensations affect anxiety. Questions included        “Certain sounds, sights, or stimuli make me feel nervous,        anxious, or on edge” and “Nothing really distracts me or makes        me anxious”. Three questions were reverse scored reflecting        higher scores that indicated greater anxiety.    -   Distractibility Variable (DV). 10 questions—derived from UCL's        Student Mental Health Survey (SENSE; McCloud et al., 2019), from        the Stanislaus State Concentration Questionnaire (CQ, 2019), and        the Cognitive Assessment Battery for ADHD Research (CAB-ADHD;        Cognifit, 2018), and interviews (Ashburner et al.,        2013)—described participant's susceptibility to distraction in        daily life. Queries included “I often begin new tasks and leave        them uncompleted”, and “If interrupted, I can switch back to        what I was doing very quickly”. Two questions were reverse        scored reflecting higher scores indicating susceptibility to        distraction.    -   Autism Spectrum Quotient (AQ-10). 10 questions—derived from the        Autism Spectrum Quotient (Allison et al., 2012)—examined only        non-autistic individuals who may have undiagnosed and/or milder        levels of autistic symptomatology (Baron-Cohen et al., 2001).        These measures were used to ensure in-group participant matching        and determine if subclinical traits existed in non-diagnosed        individuals. The AQ-10 is a subset of the larger 50-item survey        and uses a four-point Likert Scale ranging from Definitely Agree        to Definitely Disagree across 10 questions. A single point        maximum is scored for each answer and totals scores of six or        greater indicate concentrations of autistic traits.

Demographic exploration was carried out through descriptive statistics,chi-square, and Mann Whitney-U tests. Matching diagnostic groups andsignificant differences between individuals were obtained by conductingindependent sample t-tests. Serial one-way ANOVA tests revealedsignificance in anxiety and distractibility differences as they relatedto demographic features. Tukey post hoc tests confirmed wheresignificances were identified between diagnostic groups. Pearson'scorrelations established robustness among variables, and linearregressions identified predictive anxiety and distractibilitycharacteristics.

Diagnostic mediation analyses tested the hypothesis connecting anxietyas an intermediary between sensory and distractibility (See FIG. 26 ). Astatistical package was used to carry out analysis evaluatingmeasurement errors, adjusting with bootstrapping techniques (e.g.,random sampling with replacement bias-corrected and accelerated 95%confidence intervals; 1000 resamples).

A chi-square test of independence was performed to examine therelationship between gender across diagnostic groups. The relationbetween these variables was not significant at p<0.05 (χ2=2.946,p=0.229). Mann-Whitney U tests were conducted to determine whether therewas a difference in age (U=16213, p=0.948) and education (U=15461.5,p=0.356) across diagnostic groups; neither result was significant atp<0.05.

The mean distribution of anxiety and distractibility scores fordiagnostic groups across demographic variable are depicted in FIG. 29 ,which shows graphs of mean anxiety and distractibility/attention scores.A series of one-way between subjects ANOVA tests were conducted tocompare differences in anxiety and distractibility scores across variousdemographic features. The autistic group revealed a significant effectof age on both anxiety, F(3,183)=6.733, and distractibility scoresF(3,183)=10.856, both at the p<0.001* level.

Post hoc comparisons using the Tukey HSD test indicated that foranxiety, 30-39-year-olds scored significantly higher than18-21-year-olds (mean difference=0.377, p=0.008), and 40-49-year-oldsscored significantly higher than 18-21-year-olds (mean difference=0.830,p<0.001), 22-29-year-olds (mean difference=0.525, p=0.022), and30-39-year-olds (mean difference=0.453, p=0.042). For distractibility,Post hoc comparisons using the Tukey HSD test indicated that30-39-year-olds scored significantly higher than both 18-21-year-olds(mean difference=0.468, p<0.001) and 22-29-year-olds (meandifference=0.261, p=0.005), and that 40-49-year-olds scoredsignificantly higher than both 18-21-year-olds (mean difference=0.614,p=0.001) and 22-29-year-olds (mean difference=0.407, p=0.040).

There were no significant differences for non-autistic participants,including those for anxiety scores, F(3,170)=1.462, p=0.227, ordistractibility scores, F(3,170)=2.430, p=0.067, across any age group.However, there was a significant difference for non-autisticparticipants who exhibited anxiety scores and sex, F(2,171)=4.289,p=0.015. Post hoc comparisons using the Tukey HSD test indicated thatfemales scored significantly higher than males (mean difference=2.911,p=0.024); however, there was no significant differences for non-autisticparticipants' sex on distractibility scores, F(2,171)=0.514, p=0.599.

There was a significant difference in anxiety scores for autisticparticipants between sexes, F(2,184)=16.944, p<0.001, with Tukey posthoc tests revealing that non-binary participants scored significantlyhigher than male (mean difference=1.301, p<0.001) and femaleparticipants (mean difference=1.156, p<0.001). For autistic individuals,there were significant differences in distractibility between sexes,F(2,184)=6.551, p=0.002. Post hoc distractibility comparisons using theTukey HSD test indicated that non-binary, autistic participants scoredsignificantly higher than male autistic participants (meandifference=0.702, p=0.002) and female autistic participants (meandifference=0.588. p=0.015).

There was a significant effect from education on autistic individualsfor anxiety scores, F(2,184)=3.915, p=0.022; however, there was nosignificance for non-autistic participants, F(2,171)=0.148, p=0.863.Post hoc comparisons using the Tukey HSD test indicated that autisticparticipants who attended graduate school scored significantly higher onanxiety than individuals with an under-graduate degree (meandifference=3.418, p=0.016). There was no significant effect of educationon distractibility for autistic participants, F(2,184)=2.108, p=0.124;however, there was a significant effect from education ondistractibility for non-autistic participants, F(2,171)=4.360, p=0.014.Post hoc comparisons using the Tukey HSD test revealed non-autisticindividuals without a university degree scored significantly higher indistractibility than those with a degree (mean difference=0.364,p=0.016).

Table 17 shows sensory sensitivity variables and mental health scoresand statistics for diagnostic groups, including means scores and betweengroup differences.

TABLE 17 Sensory and Mental Health Autistic Non-autistic Variables (N =187) (N = 174) t(df, p) Visual, M (SD, 3.218 (.763, 1.06-4.78) 2.501(.918,0.33-4.39) t(359) = range) −8.090, p < .001* Auditory, M (SD,3.008 (.635, 1.79-5.00) 3.136 (.912, 0.43-5.00) t(359) = range) .0585, p< .559 Physiological, M 4.213 (.653, 1.83-5.00) 3.686 (.882, 1.00-5.00)t(359) = (SD, range) −6.681, p < .001* Anxiety, M (SD, 3.417 (.539,2.00-4.94) 3.283 (.731, 1.19-4.75) t(359) = range) −1.993, p < .047*Distractibility, M 3.523 (.464, 2.20-4.80) 3.427 (.763, 1.30-4.90)t(359) = (SD, range) −1.443, p < .157 *p is significant at .05

The 187 autistic participants demonstrated significantly greater levelsof visual, t(359)=−8.090, p<0.001, and physiological sensitivity,t(359)=−6.681, p<0.001, compared to the 174 non-autistic individuals.Autistic members exhibited significantly greater levels of anxiety,t(359)=−1.993, p=0.047; however, in this study there was no significanteffect for either auditory sensitivity, t(359)=0.585, p=0.559, ordistractibility scores, t(359)=1.443, p=0.157.

Tables 18 and 19 show sensitivity and outcome variable correlations fornon-autistic and autistic participants.

TABLE 18 (Pearson’s correlations for non-autistic participants) VisualAuditory Physiological Anxiety Distractibility Visual .605* .544* .678*.522* p < .01 p < .01 p < .01 p < .01 Auditory .511* .602* .435* p < .01p < .01 p < .01 Physiological .704* .549* p < .01 p < .01 Anxiety .587*p < .01 Distractibility *p is significant at .05

TABLE 19 (Pearson’s correlations for autistic participants) VisualAuditory Physiological Anxiety Distractibility Visual .172 .526* .703*.509* p = .18 p < .01 p < .01 p < .01 Auditory .255* .474* .416* p < .01p < .01 p < .01 Physiological .633* .664* p < .01 p < .01 Anxiety .798*p < .01 Distractibility *p is significant at .05

As depicted by Tables 18-19, a Pearson correlation coefficient(Bonferroni adjusted) was computed to assess the linear relationshipbetween non-autistic and autistic adults. There was a positivecorrelation for non-autistic participants indicating significancebetween each of the sensitivity and the outcome variables (mentalhealth), and all the sensory variables. There was a similarsignificantly positive correlation for autistic participants betweeneach sensitivity variable and the outcome variables (mental health);however, the relationship between visual and auditory scores failed toreach significance (r=0.172, p=0.180), while the association betweenauditory and physiology was nominally correlated (r=0.255, p<0.01). Bothdiagnostic groups exhibited correlations with the outcome variables(i.e., anxiety and fatigue).

Unique predictor variables were modeled on anxiety and attention scores(Tables 20-21). Both autistic and non-autistic visual, auditory, andphysiological variables were examined as predictors, and demographicvariables were added as covariates. For both diagnoses, education andage were not significant predictors and were excluded from furtheranalysis. Non-binary individuals were excluded due to small samplesizes. Sexes were dummy coded (i.e., 0—Male; 1—Female), and positivebeta scores indicated significance between non-autistic females andoutcome variables. All sensitivity variables and sexes uniquelypredicted non-autistic anxiety; the model accounted for 64.7% of thevariance in anxiety scores (F=81.46, p<0.001). Similarly, and forautistic individuals, all sensory variables uniquely predicted anxietyand the model accounted for 76.4% of the variance in anxiety scores(F=112.54, p<0.001). Note that sex did not contribute towards anxietyscores for the autistic group.

TABLE 20 (Model predicting anxiety for non-autistic participants) β t PVisual .241 4.96 <.001* Auditory .141 2.97  .003* Physiological .3777.90 <.001* Sex .221 3.27  .001* (R² = .647, F = 786.031, p = .0001

TABLE 21 (Model predicting anxiety for autistic participants) β t PVisual .377 13.15 <.001* Auditory .192 6.06 <.001* Physiological .3837.02 <.001* Sex .020 5.12 .606 (R² = .764, F = 142.916, p < .0001

All input variables were entered as predictors of distractibility forboth diagnostic groups, in addition to demographic variables. Fornon-autistic individuals, age and sex did not significantly predictdistractibility and was excluded from evaluation. Visual cues,physiological cues, and education significantly predicted attention.Interestingly sound sensitivity did not (p=0.239) (See Table 22). Themodel explained 40.2% of the non-autistic variance in distractibilityscores (F=28.407, p<0.001); and by augmenting the model with anxietyscores, this amount increased by 2% with and all variables (excludingsound) remaining significant predictors (See Table 23).

TABLE 22 (Model predicting distractibility for non-autisticparticipants) β t P Visual .241 3.22  .002* Auditory .077 1.18 .239Physiological .312 4.89 <.001* Education −.174 −2.68  .008* (R² = .402,F = 28.407, p < .001

TABLE 23 (Model predicting distractibility for non-autistic participants(anxiety added)) β t P Visual .138 1.96 .052 Auditory .036 .555 .580Physiological .211 2.93  .004* Education −.174 −2.73  .007* Anxiety .2842.79  .006* (R² = .429, F = 25.2, p < .001

Visual, sound, and physiological sensitivity were significant predictorsof distractibility for autistic diagnoses; however, demographicvariables were not significant (See Table 24). The non-autistic modelexplained 59.2% of the variance in distractibility scores (F=88.52,p<0.001), which increased 9% when including anxiety scores (F=98.645,p<0.001). Note, however, the visual and auditory scores were notsignificant distractibility once anxiety was included (See Table 25).

TABLE 24 (Model predicting distractibility autistic participants) β t PVisual .211 6.23 <.001* Auditory .183 5.09 <.001* Physiological .2977.38 <.001* (R² = .592, F = 88.52, p < .001

TABLE 25 (Model predicting distractibility autistic participants(anxiety added)) β t P Visual .049 1.31 .193 Auditory .059 1.64 .103Physiological .185 4.79 <.001* Anxiety .464 7.29 <.001* (R² = .684, F =98.645, p < .001

To test the hypothesis that there is an indirect effect of anxiety thatmediates the relationship between sensory sensitivity (cues) andattention (distractibility), a hierarchical multiple regression analysiswas conducted (See FIG. 30 , showing non-autistic mediation models, andFIG. 31 , autistic mediation models). There was a correction for biasand acceleration of bootstrapped confidence intervals (1000 resamples)that do not overlap with zero, all of which indicated significantrelations.

For non-autistic participants, the relationship between visualsensitivity and distractibility was significantly and directly mediatedby anxiety scale scores (b=0.247, 95% BCa CI (0.136, 0.352)). We alsoconfirmed anxiety was a significant and direct mediator betweenphysiological sensitivity and distractibility (b=0.2361, 95% BCa CI(0.1261, 0.3680)). Education was added as a covariate in both cases. Assound sensitivity was not predictive in the regression models, auditorysensitivity was not tested for in the mediation model.

For autistic individuals, there was a significant indirect effect ofanxiety for visual (b=0.3121, 95% BCa CI (0.2242, 0.4423)) and auditorysensitivity (b=0.2689, 95% BCa CI (0.1869, 0.3503)); however, the directeffect for neither was significant; hence, anxiety neither fullymediates the relationship between both visual and auditory sensitivityand distractibility for autistic individuals. However, anxietysignificantly mediated the indirect relationship between physiologicalsensitivity and distractibility (b=0.2835, 95% BCa CI (0.1990, 0.3691)),and the direct effect was significant—indicating partial mediation.

The relationship between non-autistic individuals' AQ score and studyvariables in the PPI study was examined to ensure matching both in-groupparticipants and to exclude individuals who exhibit subclinical traitsthat would influence outgroup comparisons (See Table 16).

TABLE 26 (Pearson’s correlations between AQ score and study variables)Visual Auditory Physiological Anxiety Distractibility AQ .295* .200.240* .286* .471* p < .01 p = .08 p < .01 p < .01 p < .01 *p issignificant at .05

The PPI study found that a significant positive correlation existsbetween non-autistic AQ scores and all study variables excluding sound.This suggests that individuals who score higher on the autism spectrum(those with less non-autistic traits) are more likely to experiencevisual and physiological sensitivity accompanied by higher levels ofanxiety and distractibility, consistent with between-group results.

The regression models were supplemented with non-autistic AQ to examinepredictive outcomes of anxiety and distractibility; however, there wasno significant effect (p=0.198). However, the amount of model variancedid increase 8% with the addition of AQ scores as a predictor ofdistractibility and was a significant predictor of distractibility score(p<0.001) when included within the regression model (See Table 27).

TABLE 27 (Model predicting distractibility for non-autisticparticipants) β t P Visual .107 1.708 .089 Physiological .202 3.04 .003*Education −.162 −2.79 .006* Anxiety .255 2.781 .198 AQ .140 5.261 <.001*(R² = .508, F= 34.76, p < .001

As taken from the state anxiety and state fatigue survey, means andstandard deviations for participants' level of self-rated anxiety andfatigue after the Distraction SART and the Intervention SARTs, discussedabove, are presented in Table 28. Given the data was not normallydistributed, a Wilcoxon Signed-Rank test was conducted to explorewhether the digital mediator was tolerated by participants based ontheir self-reported anxiety and fatigue.

TABLE 28 (Mean (SD) anxiety & fatigue levels between each SART) ASD: M(SD) NT: M (SD) Distraction Anxiety 3.06 (1.39) 3.43 (1.03) InterventionAnxiety: Alert 2.83 (1.15) 3.33 (0.86) Intervention Anxiety: Filter 2.61(1.09) 2.81 (0.75) Intervention Anxiety: Guidance 2.78 (1.26) 3.14(0.65) Intervention Anxiety: Combination 2.56 (1.15) 2.81 (0.87)Intervention Anxiety: Best 2.33 (1.02) 2.62 (0.80) Distraction Fatigue2.67 (1.19) 2.95 (1.05) Intervention Fatigue: Alert 2.61 (1.33) 2.95(1.05) Intervention Fatigue: Filter 2.44 (1.25) 2.81 (1.08) InterventionFatigue: Guidance 2.94 (1.35) 3.00 (0.89) Intervention Fatigue:Combination 2.47 (1.18) 2.62 (0.86) Intervention Fatigue: Best 2.28(1.13) 2.52 (0.87) N = 39. Minimum possible values were 1 and maximumpossible values were 5. Distraction Fatigue, Intervention Fatigue Alertand Intervention Fatigue Combination were each missing 1 data point.

For the non-autistic group, self-rated anxiety was significantly lowerfollowing the Filter Intervention SART (Mdn=3.0) relative to theDistraction SART (Mdn=4.0; T=10.0, Z=−2.36, p=0.02). Similarly,self-reported anxiety and fatigue were significantly lower following theCombination Intervention SART (Anxiety: Mdn=3.0; Fatigue: 2.0) incomparison to the Distraction SART (Anxiety: T=610.50, Z=−2.35, p=0.02;Fatigue: Mdn=3.0; T=10.00, Z=−1.94, p=0.05). Lower anxiety scoresrelated to a participant feeling calmer, while lower fatigue scoresrelated to a participant feeling more alert.

By contrast, there were no significant differences in anxiety or fatiguebetween the Distraction and each Intervention SART for the autisticparticipants. Given that autism is a highly heterogeneous disorder, andthe aim of this intervention is to tailor the mediations to the varyingpreferences of the individual, the analysis was subsequently conductedon participants' score from their best-performing intervention (and notaveraged mediations). Indeed, when using the best intervention, autisticparticipants' self-reported anxiety was lower in the Best InterventionSART (Mdn=2.0) than the Distraction SART (Mdn=3.50; T=5.00, Z=−2.24,p=0.02). Similarly, autistic participants' self-reported fatigue waslower in the Best Intervention SART (Mdn=2.00) compared to theDistraction SART (Mdn=2.50; T=0.00, Z=−2.33, p=0.02).

When applying the same logic to the non-autistic group in whichbest-performing intervention scores were analysed, similar results werefound. Self-reported anxiety was lower in the Best Intervention SART(Mdn=3.00) relative to the Distraction SART (T=13.00, Z=−2.78, p=0.01).Self-reported fatigue was also lower in the Best Intervention SART(Mdn=2.00) compared to the Distraction SART (T=12.00, Z=−2.32, p=0.02).

A Mann-Whitney test indicated that both diagnostic groups found the bestintervention similarly helpful as they did not significantly differ interms of the difference in their self-reported anxiety or fatiguebetween the Distraction SART and the Best Intervention SART(U_(anxiety)=174, p=0.66; U_(fatigue)=182, p=0.83) respectively.

To explore whether interventions improved performance on the SARTdespite the presence of distraction, a repeated measures ANOVA wasconducted (having checked for independence, sphericity, and normaldistribution) to compare performance between the conditions (baseline,distraction, intervention) and diagnostic group. The variables were notnormally distributed (except for EoC, but the sample size wassufficiently large such that the repeated measures ANOVA should berobust against this assumption violation. Further, as in the previousanalyses, scores from participants' best-performing intervention wereused. Means and standard deviations of variables are presented in Table29.

TABLE 29 (Mean (SD) performance between the conditions) M (SE) RT EoOBaseline 111.79 (8.34)  RT EoO Distraction 115.75 (10.05)  RT EoOIntervention 142.50 (12.28)  RT EoC Baseline 76.90 (9.00)  RT EoCDistraction 85.57 (13.33) RT EoC Intervention 154.98 (17.10)  EoOBaseline 5.17(1.32) EoO Distraction 5.33 (1.29) EoO Intervention 2.77(1.50) EoC Baseline 6.27 (0.51) EoC Distraction 7.29 (0.57) EoCIntervention 4.97 (0.44) N = 39. RT measured in milliseconds (ms).

N=39. RT measured in milliseconds (ms).

RT EoO differed significantly across the three conditions,F(1.37,50.81)=11.74, p<0.01. A post-hoc pairwise comparison usingBonferroni correction showed that RT EoO was slower in the Intervention(M=142.50, SE=12.28) relative to the Baseline (M=111.79, SE=8.34,p<0.01) and Distraction (M=115.75, SE=10.05, p<0.01). However, there wasno main effect of group F(1,37)=1.40, p=0.25, and no interaction betweencondition and diagnostic group, F(2,74)=0.56, p=0.51.

Similarly, RT EoC significantly differed across the three conditions,F(1.52, 56.36)=16.92, p<0.01. A post-hoc pairwise comparison usingBonferroni correction showed that RT EoC significantly increased betweenthe Baseline (M=76.90, SE=9.00) and Intervention (M=154.98, SE=17.10) aswell as between the Distraction (M=85.57, SE=13.33) and Intervention(p<0.01). However, there was no main effect of group F(1, 37)=3.00,p=0.09 or an interaction between condition and diagnostic group, F(2,74)=1.12, p=0.32.

Having met both assumptions of normality and sphericity, a repeatedmeasures ANOVA determined that EoC differed significantly across thethree conditions, F(2,74)=12.5, p<0.01. A post hoc pairwise comparisonusing Bonferroni correction showed fewer EoC in the Intervention(M=4.97, SE=0.44) relative to both the Baseline (M=6.27, SE=0.51,p=0.02) and Distraction (M=7.29, SE=0.57, p<0.01). However, there was nomain effect of group F(1, 37)=0.00, p=0.95, or an interaction betweencondition and diagnostic group, F(2,74)=0.39, p=0.68.

Finally and surprisingly, EoO did not significantly differ across thethree conditions, F(1.38, 50.95)=2.54, p=0.11, nor was there a maineffect of group F(1, 37)=0.19, p=0.67, nor an interaction betweencondition or diagnostic group, F(2, 74)=1.89, p=0.17.

To determine whether the performance and self-reported mental healthdata correlated with one another, a Spearman's rank correlation (giventhe data was non-normal) was conducted. Table 30, below, depicts theresults.

TABLE 30 (Spearman’s Rank Correlation: Ecological & PhysiologicalMeasures) 1 2 3 4 5 6 Autistic 1. Best Anxiety Intervention Score 2.Best Fatigue 0.71** Intervention Score 3. Best EoC RT −0.05 −0.02 4.Best EoC RT −0.36 −0.25  0.35 5. Best EoC −0.18  0.06 −0.30 −0.10 6.Best EoC  0.177 −0.15 −0.35 −0.51*  0.075 Non- 1. Best Anxiety autisticIntervention Score 2. Best Fatigue  0.38 Intervention Score 3. Best EoCRT  0.04  0.27 4. Best EoO RT  0.15  0.15 0.79** 5. Best EoC −0.34−0.56** −0.45* −0.46* 6. Best EoO −0.02  0.12  0.35  0.09 −0.13 N = 39;*p < 0.05 (two-tailed); **p < 0.001 (two-tailed). Bold indicatescorrelation is significant at alpha level corrected by Bonferronimethod. The diagnostic groups were analyzed independently to preventcorrelations of the whole sample concealing any diverging underlyingtrends. Indeed, different correlations were revealed between the twogroups.

For the autistic group, best fatigue intervention (the intervention thatproduced the lowest fatigue rating) scores were correlated with bestanxiety intervention (the intervention that produced the lowest anxietyrating) scores (r_(s)=0.71, p<0.01, N=18), such that lower anxiety wasassociated with lower fatigue in the Intervention SART. Similarly, bestEoO was negatively correlated with best EoC (r_(s)=−0.51, p=0.03, N=18),such that more errors of omission were associated with fewer errors ofcommission. No performance variables appeared to correlate with anyself-reported mental health variables in the autistic group.

In contrast, the correlations in the non-autistic group revealed adifferent pattern. Best EoC was correlated with both RT EoO(r_(s)=0-0.46, p=0.04, N=21) and RT EoC (r_(s)=−0.45, p=0.04, N=21),such that more errors of commission were associated with reducedreaction time for errors of omission and understandably, reducedreaction time of errors of commission. Best RT EoO was also highlyassociated with best RT EoC (r_(s)=0.79, p<0.01, N=21). Interestingly,best EoC was also correlated with best Fatigue Intervention Scores(r_(s)=−0.56, p<0.01, N=21), suggesting when participants felt lessfatigued, they made fewer errors of commission.

A multiple linear regression with enter method was used to predict EOCperformance in the Intervention SART from participants' anxiety andfatigue levels, education level, diagnostic group, and best interventiontype. A preliminary analysis suggested that the assumption of normaldistribution was met (Shapiro-Wilk test; p=0.17). Further the assumptionof multicollinearity was met given none of the predictor variablescorrelated with each other by more than 0.7. However, none of thepredictor variables correlated with the dependent variables by more than0.3. A Cook's Distance test revealed that there was one influential datapoint which was then excluded from the analysis to ensure the Cook'sdistance remained below 1 and the Standard Residual remained between −3and 3. The model did not explain a statistically significant amount ofvariance in EOC performance, F(5, 32)=0.83, p=0.54, R²=0.12, R²_(adjusted)=−0.02. Each variable is presented in Table 31.

TABLE 31 (Multiple Regression Predicting EOC performance in theIntervention SART from Anxiety, Fatigue, Education, Diagnostic Group andBest Intervention Type) B SE B β Anxiety 0.43 0.59 0.15 Fatigue 0.660.56 0.25 Education 0.48 0.98 0.09 Diagnostic group 0.08 0.95 0.2Intervention type 0.03 0.07 0.07 N = 38; *p < 0.05 (two-tailed); **p <0.01 (two-tailed).

A multinominal logistic regression was performed to model therelationship between diagnostic group, age, education, and gender withthe best performance intervention. As shown in Table 32, this regressionwas repeated such that the reference category was varied between theintervention types (alert, filter, guidance, combination).

TABLE 32 (Multinominal Logistics Regression Predicting Best PerformanceIntervention from Diagnostic Group, Age, Education & Gender) χ² (Alert)χ² (Filter) χ² (Guidance) χ² (Comb) df Diagnostic group 16.95* 16.95*16.95* 16.95* 7 Age 12.68 12.68 12.68 12.68 7 Education 18.31** 18.31**18.31** 18.31** 7 Gender 7.63 7.63 7.63 7.63 7 N = 39; *p < 0.05(two-tailed); **p < 0.01 (two-tailed). Parentheses denote the referencecategory

The regressions produced the same results. The addition of thepredictors to the model significantly improved the fit between the modeland the data, χ² (28, N=39)=43.40, Nagelkerke R²=0.70, p=0.03.Significant unique contributions were made by Diagnostic Group(χ²=16.95, p=0.02) and Education (χ²=18.31, p=0.01). Goodness of fit wasexplored through the Pearson chi-square statistic which indicated a goodfit since this was a non-significant result, χ²=141.61, p=1.00.

Post-hoc Chi-square of Independence tests were subsequently run toidentify the characteristics of diagnostic group and education that wereassociated with best intervention type. It appeared that there was not asignificant relationship between diagnostic group (autistic vs.non-autistic) and best intervention type, χ² (7, N=39)=3.87, p=0.80),nor was there a significant relationship between education (universityvs non-university) and best intervention type, χ² (7, N=38)=11.43,p=0.12).

A binary logistic regression was conducted to ascertain the effects ofdiagnostic group, age, gender, or education on the likelihood thatparticipants performed best using alerts. A preliminary analysissuggested that the assumption of multicollinearity was met(tolerance=Diagnostic Group: 0.88; Age: 0.67; Gender: 0.93; Education:0.62). An inspection of standardized residual values revealed that therewere two outliers (Std. residual—2.24; 2.56), which were kept in thedataset. The model was not statistically significant, χ² (5, N=39)=4.82,p=0.44 suggesting that it could not distinguish between those whoperformed best with alerts and those who did not. The model explainedbetween 11.6% (Cox & Snell R square) and 16.4% (Nagelkerke R square) ofthe variance in the dependent variable and correctly classified 69.2% ofcases. As shown in Table 33, none of the predictors significantlycontributed to the model.

TABLE 33 (Logisitic Regression Predicting Best Intervention Type Alertfrom Diagnositc Group, Age, or Education) B SE Wald df p OR DiagnosticGroup 0.03 0.80 0.00 1 0.97 1.03 Age −0.02 0.06 0.08 1 0.78 0.98Education 1.73 0.98 3.15 1 0.08 5.66 N = 39; *p < 0.05 (two-tailed); **p< 0.01 (two-tailed).

A binary logistic regression was conducted to ascertain the effects ofdiagnostic group, age, gender, or education on the likelihood thatparticipants performed best using filters. A preliminary analysissuggested that the assumption of multicollinearity was met(tolerance=Diagnostic Group: 0.88; Age: 0.67; Gender: 0.93; Education:0.62). An inspection of standardised residual values revealed that therewere no outliers. The model was not statistically significant, χ² (5,N=39)=5.93, p=0.31 suggesting that it could not distinguish betweenthose who performed best with filters and those who did not. The modelexplained between 14.1% (Cox & Snell R square) and 19.6% (Nagelkerke Rsquare) of the variance in the dependent variable and correctlyclassified 66.7% of cases. As shown in Table 34, none of the predictorssignificantly contributed to the model.

TABLE 34 (Logisitic Regression Predicting Best Intervention Type Filterfrom Diagnositc Group, Age, or Education) B SE Wald df p OR DiagnosticGroup 1.30 0.79 2.68 1 0.10 3.66 Age −0.09 0.08 1.33 1 0.25 0.92Education 0.57 0.92 0.39 1 0.53 1.79 N = 39; *p < 0.05 (two-tailed); **p< 0.01 (two-tailed).

A binary logistic regression was conducted to ascertain the effects ofdiagnostic group, age, gender, or education on the likelihood thatparticipants performed best using guidance. A preliminary analysissuggested that the assumption of multicollinearity was met(tolerance=Diagnostic Group: 0.88; Age: 0.67; Gender: 0.93; Education:0.62). An inspection of standardised residual values revealed that therewere no outliers. The model was not statistically significant, χ² (5,N=39)=2.64, p=0.76 suggesting that it could not distinguish betweenthose who performed best with guidance and those who did not. The modelexplained between 6.5% (Cox & Snell R square) and 9.4% (Nagelkerke Rsquare) of the variance in the dependent variable and correctlyclassified 76.9% of cases. As shown in Table 35, none of the predictorssignificantly contributed to the model.

TABLE 35 (Logisitic Regression Predicting Best Intervention TypeGuidance from Diagnositc Group, Age, or Education) B SE Wald df p ORDiagnostic Group 0.27 0.79 0.11 1 0.74 1.30 Age 0.05 0.06 0.71 1 0.401.05 Education −0.18 0.90 0.04 1 0.84 0.84 N = 39; *p < 0.05(two-tailed); **p < 0.01 (two-tailed).

A binary logistic regression was conducted to ascertain the effects ofdiagnostic group, age, gender, or education on the likelihood thatparticipants performed best using a combination of the interventions. Apreliminary analysis suggested that the assumption of multicollinearitywas met (tolerance=Diagnostic Group: 0.88; Age: 0.67; Gender: 0.93;Education: 0.62). An inspection of standardised residual values revealedthat there were no outliers. The model was not statisticallysignificant, χ² (5, N=39)=0.39, p=1.00 suggesting that it could notdistinguish between those who performed best with combinations and thosewho did not. The model explained between 1.0% (Cox & Snell R square) and1.4% (Nagelkerke R square) of the variance in the dependent variable andcorrectly classified 66.7% of cases. As shown in Table 36, none of thepredictors significantly contributed to the model.

TABLE 36 (Logisitic Regression Predicting Best Intervention TypeCombination from Diagnositc Group, Age, or Education) B SE Wald df p ORDiagnostic Group 0.18 0.73 0.06 1 0.81 1.19 Age −0.02 0.06 0.15 1 0.700.98 Education 0.14 0.85 0.03 1 0.94 0.90 N = 39; *p < 0.05(two-tailed); **p < 0.01 (two-tailed).

A series of one-way ANOVA tests were conducted to determine whetherthere were significant differences in fatigue and distractibility scoresbased on the various demographic categories. Having met the assumptionof homogeneity of variance for the non-autistic group, there was nosignificant effect of age on fatigue, F(3, 158)=0.43, p=0.73, ordistractibility, F(3, 158)=2.23, p=0.09. In contrast, for the autisticgroup, the assumption of homogeneity of variance was violated.Therefore, the Welch test was used to conduct the analysis. This showedthat there was a significant effect of age on fatigue for the autisticgroup, t(30.78)=12.53, p<0.01 as well as on distractibility,t(32.99)=7.83, p<0.01. Post-hoc comparisons using the Games-Howell testrevealed that for fatigue, the 30-39-year-olds scored significantlylower than 18-21-year-olds (mean difference=1.01, p<0.01), andsignificantly lower than the 22-29-year-olds (mean difference=0.49,p=0.01). Further, the 41-49-year-olds scored significantly lower thanthe 18-21-year-olds (mean difference=1.26, p<0.01) and significantlylower than the 22-29-year-olds (mean difference=0.74, p=0.04). Fordistractibility, the post-hoc Games-Howell test revealed that41-49-year-olds scored significantly higher on distractibility comparedwith the 18-22-year-olds (mean difference=0.61, p<0.01) and the22-29-year-olds (mean difference=0.41, p=0.02). Further, the31-39-year-olds scored significantly higher than the 18-22-year-olds(mean difference=0.47, p=0.01) as well as the 22-29-year-olds (meandifference=0.26, p=0.02).

Given the assumption of homogeneity of variance based on the median wasmet for both diagnostic groups, a one-way ANOVA revealed that there wereno significant differences between the genders on their fatigue scores(Autistic: F(2, 184)=2.83, p=0.06; Non-autistic: F (2, 159)=0.85,p=0.43). Similarly, in the non-autistic group, the genders did notsignificantly differ on their distractibility scores, F(2, 159)=0.46,p=0.64. However, in the autistic group, the genders did significantlydiffer on their distractibility scores F(2, 184)=6.55, p<0.01. Apost-hoc Tukey test revealed that autistic participants who attendedgraduate school scores significantly more on distractibility than thosewho did not have a degree (mean difference=−0.70, p<0.01) and those whohad a degree (mean difference=−0.59, p=0.02).

Finally, for the non-autistic group, there were no significantdifferences between the educational groups on their fatigue scores F(2,159)=0.56, p=0.57. However, the educational groups did significantlydiffer on their distractibility scores in the non-autistic group, F(2,159)=4.47, p=0.01. A post-hoc Tukey test revealed that those without adegree scored significantly higher on distractibility than those whoattended graduate school (mean difference=0.40, p=0.01). For theautistic group, the educational groups significantly differed in theirfatigue scores F(2, 184)=5.03, p=0.01. A post-hoc Tukey test revealedthat those participants who had attended graduate school scoredsignificantly lower on fatigue than those without a degree (meandifference=0.64, p=0.01). In terms of distractibility, the autisticeducational groups did not significantly differ on the Welch test,t(41.93)=1.85, p=0.17.

A one-way ANOVA revealed that there were no significant differencesbetween the genders on their fatigue scores (Autistic: F(2, 184)=2.83,p=0.06; Non-autistic: F (2, 159)=0.85, p=0.43). Similarly, in the non-ysignificantly differed in their fatigue scores F(2, 184)=5.03, p=0.01. Apost-hoc Tukey test revealed that those participants who had attendedgraduate school scored significantly lower on fatigue than those withouta degree (mean difference=0.64, p=0.01). In terms of distractibility,the autistic educational groups did not significantly differ on theWelch test, t(41.93)=1.85, p=0.17.

Table 37 shows the mean scores of the three sensory sensitivityvariables, as well as the mean anxiety, fatigue, and attention scoresacross the two diagnostic groups. Independent samples t-tests wereconducted to compare the scores on each of the variables across thediagnostic groups. Autistic adults reported significantly greater levelsof visual sensitivity, t(313.31)=−7.83, p<0.01, and physiologicalsensitivities, t(292.46)=−6.09, p<0.01, compared with the non-autisticgroup. Autistic individuals also reported lower levels of fatigue(meaning they tend to be more alert) in comparison to the non-autisticpopulation, t(347)=5.13, p<0.01. There were no significant differencesbetween the groups on their mean scores on the other variables.

TABLE 37 (Mean Sensitivity Scores and between group differences) ASD: M(SD) NT: M (SD) t(df) Visual 3.22 (0.76) 2.50 (0.92) −7.83 (313.31)**Auditory 3.01 (0.63) 3.14 (0.93)   0.64 (278.27)  Physiological 4.21(0.65) 3.70 (0.88) −6.09 (292.46)** Anxiety 3.42 (0.54) 3.30 (0.74)−1.71 (289.77)  Fatigue 2.24 (0.75) 2.67 (0.79)   5.13 (347)** Distractibility 3.52 (0.46) 3.42 (0.78) −1.40 (253.63)  N = 349; *p <0.05 (two-tailed); **p < 0.01 (two-tailed).

Table 38 shows Bonferroni adjusted Spearman rank correlations betweenthe variables for both the non-autistic and autistic groups. Applicationof the Kolmogorov-Smirnov test revealed that most variables, except forvisual and anxiety in the non-autistic group, were non-normal. For thenon-autistic group, significant associations were found between each ofthe sensory sensitivity variables and the outcome variables (anxiety,fatigue, and distractibility), as well as between all the sensorysensitivity variables. A similar pattern of association was found forthe autistic group, except for the relationship between visual andauditory which was not significant (r=0.11, p=0.13).

TABLE 38 (Spearman’s Rank Correlation) 1 2 3 4 5 6 Autistic 1. Visual 2.Auditory  0.11 3. Physiological  0.35**  0.19** 4. Anxiety  0.73** 0.40** 0.41** 5. Fatigue −0.69** −0.27** −0.45** −0.81** 6.distractibility  0.52**  0.36**  0.49**  0.70** −0.71** Non- 1. Visualautistic 2. Auditory  0.58** 3. Physiological  0.52**  0.46** 4. Anxiety 0.66**  0.56**  0.64** 5. Fatigue −0.56** −0.50** −0.52** −0.76** 6.distractibility  0.49**  0.40**  0.50**  0.55** −0.43** N = 349; *p <0.05 (two-tailed); **p < 0.01 (two-tailed). Bold indicates correlationis significant at alpha level corrected by Bonferroni method. Thediagnostic groups were analysed independently to prevent correlations ofthe whole sample concealing any diverging underlying trends. Indeed,different correlations were revealed between the two groups.

Given the significant associations between the variables in Table 38, aseries of linear regressions were conducted to analyse the uniquecontribution of each variable when predicting fatigue anddistractibility, having first checked for multicollinearity. Visual,sound, and physiological sensitivity variables as well as demographicvariables (age, gender, education) were added as predictors of fatiguescores. For the non-autistic group, Table 39 shows that while thedemographic variables did not predict fatigue, all the sensory variablesuniquely predicted fatigue, with the model accounting for 47% of thevariance in fatigue (F(6, 155)=23.07, p<0.01). For the autistic group,Table 40 shows that all the sensory variables as well as age uniquelypredicted fatigue, although education and gender did not (F(6,180)=64.39, p<0.01). The model accounted for 68% of the variance infatigue.

TABLE 39 (Non-autistic: Regression predicting fatigue) B SE B β Visual−0.26 0.07 −0.30** Auditory −0.15 0.07 −0.18* Physiological −0.31 0.07−0.34** Age 0.02 0.07 0.02 Gender 0.04 0.09 0.02 Education −0.07 0.07−0.06 N = 162; *p < 0.05 (two-tailed); **p < 0.01 (two-tailed). R² =0.47 F = 23.07, p < 0.01

TABLE 40 (Autistic: Regression predicting fatigue) B SE B β Visual −0.440.05 −0.45** Auditory −0.13 0.05 −0.11** Physiological −0.37 0.06−0.32** Age −0.17 0.05 −0.17** Gender −0.09 0.06 −0.07  Education −0.060.06 −0.04  N = 187; *p < 0.05 (two-tailed); **p < 0.01 (two-tailed). R²= 0.68 F = 64.39, p < 0.01

Table 41 shows a regression predicting distractibility from the sensoryvariables as well as the demographic variables for the non-autisticgroup. The model predicted 41.9% of the variance in distractibility,F(6, 155)=18.65, p<0.01. Both visual and physiological sensory variableswere significant unique predictors of fatigue. In contrast, as depictedby Table 42, the model predicting distractibility for the autistic groupshowed a different pattern, F(6, 180)=47.35, p<0.01 and predicted 61.2%of the variance in distractibility. All variables bar education weresignificant unique predictors of distractibility.

TABLE 41 (Non-autistic: Regression predicting distractibility) B SE B βVisual 0.23 0.07 0.27** Auditory 0.08 0.07 0.10 Physiological 0.31 0.070.35** Age −0.13 0.08 −0.11 Gender −0.06 0.09 −0.04 Education −0.14 0.07−0.12 N = 162; *p < 0.05 (two-tailed); **p < 0.01 (two-tailed). R² =0.42, F = 18.65, p < 0.01

TABLE 42 (Autistic: Regression predicting distractibility) B SE B βVisual 0.19 0.04 0.32** Auditory 0.19 0.04 0.26** Physiological 0.270.04 0.37** Age 0.07 0.03 0.12* Gender 0.09 0.04 0.11* Education −0.030.04 −0.04 N = 187; *p < 0.05 (two-tailed); **p < 0.01 (two-tailed). R²= 0.61, F = 47.35, p < 0.01

The next regression predicted distractibility again but includedfatigue. For the non-autistic group, the model predicted 43.2% of thevariance in distractibility, F(7, 154)=16.72, p<0.01. Visual,physiological and education were significant unique predictors ofdistractibility. However, the other variables, including fatigue werenot significant predictors of distractibility. In contrast, for theautistic group, the regression predicting distractibility includingfatigue showed that, not only did the model predict 70.5% of thevariance in distractibility (F(7, 179)=61.19, p<0.01), but that fatigue,auditory and physiological sensory variables were unique predictors ofdistractibility. Table 43 shows the results for the non-autistic group.Table 44 shows the results for the autistic group.

TABLE 43 (Non-autistic: Regression predicting distractibility (addingfatigue)) B SE B β Visual 0.19 0.07 0.22** Auditory 0.06 0.07 0.07Physiological 0.27 0.07 0.30** Age −0.13 0.08 −0.11 Gender −0.06 0.09−0.04 Education −0.15 0.07 −0.13* Fatigue −0.15 0.08 −0.15 N = 162; *p <0.05 (two-tailed); **p < 0.01 (two-tailed). R² = 0.43, F = 16.72, p <0.01

TABLE 44 (Autistic: Regression predicting distractibility (addingfatigue)) B SE B β Visual 0.04 0.04 0.07 Auditory 0.15 0.03 0.20**Physiological 0.14 0.04 0.20** Age 0.01 0.03 0.02 Gender 0.06 0.04 0.07Education −0.05 0.04 −0.06 Fatigue −0.34 0.05 −0.54** N = 187; *p < 0.05(two-tailed); **p < 0.01 (two-tailed). R² = 0.71, F = 61.19, p < 0.01

Mediation analysis was performed to explore the indirect effect offatigue on the relationship between distractibility and auditory as wellas physiological sensitivity variables (given they were both uniquepredictors of distractibility). The analysis was only run for theautistic group given that fatigue did not appear to be a significantpredictor of distractibility for the non-autistic group. First, thedirect effect of auditory and then separately, physiology on fatigue wasthen calculated through a bivariate regression. There was a significantresult for both (auditory: B=−0.35, SE B=0.08, p<0.01; physiology:B=−0.77, SE B=0.06, p<0.01). A multiple regression including bothfatigue and auditory (fatigue: B=−0.46, SE B=0.03, p<0.01; auditory:B=0.14, SE B=0.03, p<0.01), as well as a separate regression includingboth fatigue and physiology (fatigue: B=−0.39, SE B=0.04, p<0.01;physiology: B=0.17, SE B=0.04, p<0.01) were run to predictdistractibility. The Sobel test was then conducted to test the indirecteffect for statistical significance for each model. For the modelincluding auditory, as shown in FIG. 32 , the indirect effect is z=4.13,p<0.01, concluding that a partial mediation occurred between auditorysensory on distractibility via fatigue. For the model includingphysiology, as shown in FIG. 33 , the indirect effect is z=8.12, p<0.01,suggesting that a partial mediation occurred between physiology sensoryon distractibility via fatigue.

Given that physiological sensory sensitivity, fatigue and AQ scores werenon-normally distributed, a Spearman's Rank correlation was conducted todetermine the relationship between AQ score and the study variables(Table 45).

TABLE 45 (Spearman’s Rank Correlation: AQ Score and Study Variables)Visual Auditory Physiological Anxiety Fatigue Distractibility AQ 0.090.13 0.03 0.06 0.16 0.38** N = 140; *p < 0.05 (two-tailed); **p < 0.01(two-tailed). Bold indicates correlation is significant at alpha levelcorrected by Bonferroni method.

AQ scores were only collected from 140 non-autistic individuals in thestudy, therefore only these participants were included in the analysis.Distractibility was the only variable significantly associated with AQscores (r=0.38, p<0.01), suggesting that those non-autistic individualswith higher autistic symptomology were more likely to score higher ondistractibility.

AQ score was then included in the regression models predictingdistractibility in non-autistic participants. The results are shown inTable 46. The model accounted for 53% of the variance indistractibility, F(8, 131)=18.07, p<0.01. Visual, physiological andeducation remained significant predictors of distractibility. AQ scoretoo appeared to be a significant predictor of distractibility.Interestingly though, in comparison to the model without AQ (Table 43),fatigue now appeared to be a significant predictor of distractibilitywhen AQ was included in the model. Such a change implies there may be aconfounding effect of AQ.

TABLE 46 (Non-autistic: Regression predicting distractibility (addingAQ)) B SE B β Visual 0.21 0.08 0.24** Auditory −0.02 0.07 −0.02Physiological 0.22 0.07 0.25** Age −0.08 0.07 −0.07 Gender −0.01 0.090.01 Education −0.15 0.07 −0.14* Fatigue −0.24 0.08 −0.25** AQ Score0.14 0.03 0.33** N = 162; *p < 0.05 (two-tailed); **p < 0.01(two-tailed). R² = 0.53, F = 18.07, p < 0.01

FIGS. 34A-34B show summary results of the PPI study described herein,which provided a basis for conducting and refining the SART/WOz clinicalstudy described herein. As depicted by FIG. 34A, different associationsbetween individual demographics (e.g., age sex, and education) andanxiety, fatigue, and/or focus were found in the autistic group versusthe neurotypical group. Additionally, it was found that the autisticgroup tended to be more sensitive to visual and physiological stimuli,and less fatigued. As depicted by FIG. 34B, it was found that in somecases anxiety and fatigue mediated sensory sensitivity in a differentmanner for the autistic group versus the non-autistic group. Forexample, whereas for autistic individuals it was found that fatiguesignificantly mediated the indirect and direct relationship betweenauditory sensitivity and distractibility, no such relationship was foundfor non-autistic individuals. Some of the findings of the regressionanalysis in the PPI study included:

-   -   For Distractibility Only Prediction and for non-autistic        participants, both visual, physiological, and education were        predictors. For autistic participants, all variables were        predictors, except for education.    -   For Fatigue Only Predictions and for non-autistic participants,        all sensory variables were predictors (no demographics        predicted). For autistic participants, all variables were        predictors, except for gender and education.    -   For Anxiety Only Predictions and for non-autistic participants,        all sensory variables were predictors and gender. For autistic        participants, all variables were predictors, but no        demographics.    -   For Fatigue and Anxiety Predictions and for non-autistic        participants, anxiety and gender were predictors. For autistic        participants, all visual, physiological, anxiety, and age were        predictors.    -   For Distractibility and Fatigue Prediction for non-autistic        participants, visual, physiological and education were        predictors (demographics did not). For autistic participants,        auditory, physiological, and fatigue were predictors.    -   For Distractibility and Anxiety Prediction for non-autistic        participants, physiological, education, and anxiety were        predictors. For autistic participants, physiological and anxiety        were predictors.    -   For Distractibility, Fatigue and Anxiety Prediction for        non-autistic participants, visual, physiological, and anxiety        were predictors. For autistic participants, auditory,        physiological, anxiety, and fatigue were predictors.    -   For Distractibility Only Prediction with AQ-10 tests and for        non-autistic participants only, physiological, education,        anxiety, and AQ were predictors. Autistic participants were        excluded as they did not take the AQ-10.

FIGS. 35A-35B show summary results of the SART/WOz clinical studydescribed herein. As described above, the study involved multiple trialsthat tested different mediations/interventions and different sensorycues. As depicted by FIG. 35A, all mediations/interventions (e.g.,alert, filter, guidance, or combination hereof) were found to improveanxiety in both autistic and non-autistic individuals. By contrast, onlysome mediations (e.g., filters, or combination) were found to improvefatigue whereas others (e.g., guidance) were found to potentially bedetrimental. In either case, mediations that were customized toparticular individuals for each group were found to be the mosteffective. Although it was initially expected that only individuals inthe autistic diagnoses group (i.e., the experiment group) would be thebeneficiaries of digital mediations, surprisingly performanceimprovements were also present in the control group of non-autisticparticipants. As such, the benefits of increased performance by fewererrors, improved timing, and/or relief from anxiety and fatigue wasfound in both groups.

As depicted by FIG. 35B, overall performance for all users, as measuredby a reduction in EoC, a reduction in EoO, a better reaction time(slower) for EoC, and response time (slower) for EoO, improved with apersonalized mediation. In this case, the percentage change is measuredin the change in errors or increase in reaction time/response time. Asdepicted “baseline to distraction” shows the change from baseline (nodistracting cues present) to a distraction added. Performance wasimproved for all users when mediations were personalized, regardless ofwhether the mediation was added to baseline or after a distraction. Asdiscussed above, it is anticipated that one of the mechanisms ofachieving performance improvement is that by virtue of a personalizedmediation causing a user to slow down (e.g., as measured by an increasein reaction time) and experience greater mindfulness, the participantstays focused for longer. This was particularly found to be the case inautistic participants. In practice, and depending on a given task theuser is performing, this increase in response/reaction time can be onthe order of tens of ms (e.g., 50 ms).

As the foregoing PPI study and SART/WOz clinical study illustrate, theoptimal mediations for a given user can be predicted given data specificto the user and correlations between user performance and usercharacteristics such as demographics, sensory sensitivities, and statusas autistic or non-autistic. As such, the multi-assistive wearabletechnology described herein can better initialize and/or optimize theone or more sensory thresholds and mediations associated with a user.For example, FIG. 36A is an operational flow diagram illustrating anexample method 3600 for initializing and iteratively updating one ormore sensory thresholds and one or more mediations associated with aspecific user. In some implementations, method 3600 can be implementedby one or more processors (e.g., one or more processors of wearabledevice 10 and/or mobile device 20) of a wearable device system executinginstructions stored in one or more computer readable media (e.g., one ormore computer readable media of wearable device 10 and/or mobile device20).

Operation 3601 includes obtaining demographic data of the user of thewearable device. This can include receiving user input at a userinterface indicating an age, education level, gender, or otherdemographic data of the user.

Operation 3602 includes obtaining user sensory sensitivity dataindicating whether the user is visually sensitive, sonically sensitive,or interoceptively sensitive. This can include receiving user input atthe user interface indicating whether the user of the wearable device isvisually sensitive, sonically sensitive, or interoceptively sensitive.In some cases, the user input can includes input at a GUI including oneor more responses by the user to one or more prompts that are indicativeof whether the user is visually sensitive, sonically sensitive, and/orinteroceptively sensitive. These responses can indicate user preferencesto certain sensory inputs such as stimuli that the user prefers, stimulithat make the user uncomfortable, the user's perceived and/or measuredsensitivity to different stimuli, and the like. In some implementations,the response can include responses to questions as described withreference to the studies discussed above.

Operation 3603 includes obtaining neurodiversity data indicating whetherthe user is neurodiverse or neurotypical. For example, theneurodiversity data can indicate whether the user is autistic ornon-autistic. In some implementations, the system can store a firstidentifier that indicates whether the user is neurodiverse orneurotypical. In some implementations, the neurodiversity data can beobtained by user input at a user interface indicating whether the userhas been diagnosed as neurotypical. In other implementations, furtherdiscussed below, the wearable device system can be configured to performa method for providing a diagnostic prediction of whether the user isneurodiverse or neurotypical.

Operation 3604 includes initializing and storing the one or more sensorythresholds and one or more mediations associated with the user. Thethresholds and mediations associated with the user can be based on theuser sensory sensitivity data, the demographic data, and/or theneurodiversity data. In some cases, the demographic data can be ignored.

Operation 3605 includes collecting sensor data and environmental datawhile the user wears the wearable device.

Operation 3606 includes in response to collecting the sensor data and/orenvironmental data while the user wears the wearable device, modifyingthe one or more sensory thresholds and the one or more mediationsassociated with the user. As depicted, operations 3605-3606 can iterateover time as the user utilizes the wearable device system to providesensory relief. The frequency with which the one or more sensorythresholds and the one or more mediations are updated in response tonewly-collected data can be configurable, system-defined, and/oruser-defined. For example, updates can depend on the amount of data thatis collected and/or the amount of time that has passed. In someimplementations, operations 3605-3606 can be skipped. For example, theuser can disable updating the thresholds and/or mediations based onactual use of the wearable device.

As the foregoing PPI study and SART/WOz clinical study also illustrate,anxiety and fatigue can mediate sensory sensitivity in a differentmanner for autistic versus non-autistic users. In some implementations,the features found to be correlated with autistic versus non-autisticusers can provide a basis for training a model that given, specificfeatures corresponding to a user (e.g., sensory sensitivity features,anxiety features, fatigue features, demographic features, etc.) outputsa prediction (e.g., as a likelihood/probability) that a user is autisticor not autistic. For example, FIG. 36B and FIG. 36C are operational flowdiagrams illustrating example methods 3610, 3620 for predicting whethera user is neurodiverse (e.g., autistic) or neurotypical. In someimplementations, method 3610 or method 3620 can be implemented by one ormore processors (e.g., one or more processors of wearable device 10and/or mobile device 20) of a wearable device system executinginstructions stored in one or more computer readable media (e.g., one ormore computer readable media of wearable device 10 and/or mobile device20).

In method 3610, operation 3602 can be performed as discussed above.Operation 3611 includes deriving, based on the sensory sensitivity data,one or more sensory sensitivity scores including a visual sensitivityscore, a sonic sensitivity score, and/or an interoceptive sensitivityscore. For example, based on the user's response to the prompts, one ormore scores (e.g., normalized on a scale such as 0-100) can be derived.Operation 3612 includes obtaining anxiety data measuring a generalanxiety level of the user. For example, this can include receiving at aGUI one or more responses by the user to one or more prompts indicatingan anxiety level of the user in different contexts. Operation 3613includes deriving, based on the anxiety data, an anxiety score. Forexample, based on the user's response to the prompts, a score (e.g.,normalized on a scale such as 0-100) can be derived. Operation 3614includes predicting, using a trained model, based on the one or moresensory sensitivity scores and the anxiety score, a likelihood that theuser is neurodiverse. The model can be configured/trained to predict aprobability of autism based at least on features includes an anxietylevel/score and one or more sensory sensitivity levels/scores of a givenuser. Each of the features can be weighted differently. It should benoted that the model can also be trained to consider other features(e.g., demographic data) when making the prediction. Operation 3615includes making a determination that user is neurodiverse orneurotypical, and storing an associated identifier. For example, if theprediction output by the model meets a threshold (e.g., >80%probability), a prediction that a user is autistic can be made. In someimplementations, the system can validate the prediction by measuring theuser's performance in response to certain tasks when mediations arepresent and not present. This performance can be measured using thewearable device and/or mobile device by administering SARTs as discussedabove. The level of improvement in the user's performance, given aparticular mediation, can further validate whether the predicteddiagnosis is correct or incorrect.

In method 3620, operations 3602, 3611, and 3615 can be performed asdescribed above. Operation 3621 includes obtaining fatigue datameasuring a general fatigue level of the user 3621. For example, thiscan include receiving at a GUI one or more responses by the user to oneor more prompts indicating a fatigue level of the user in differentcontexts. Operation 3622 includes deriving, based on the fatigue data, afatigue score. For example, based on the user's response to the prompts,a score (e.g., normalized on a scale such as 0-100) can be derived.Operation 3623 includes predicting, using a trained model, based on theone or more sensory sensitivity scores and the fatigue score, alikelihood that the user is neurodiverse 3623. The model can beconfigured/trained to predict a probability of autism based at least onfeatures includes a fatigue level/score and one or more sensorysensitivity levels/scores of a given user. Each of the features can beweighted differently. It should be noted that the model can also betrained to consider other features (e.g., demographic data) when makingthe prediction. In some implementations, both anxiety and fatiguefeatures can be considered in the trained model.

In some implementations, the multi-sensory, assistive wearabletechnology described herein can be implemented using a network topologythat ensures user data privacy and facilitates ethical relationshipsamong device layers, systems, and stakeholders such as the user/wearer,the user's family, the user's therapist, and/or the user's generalpractitioner. To this end, edge and fog computing can be implementedusing devices localized at the system's perimeter to facilitate andsecure any cloud connectivity using devices localized at the system'sperimeter. These devices can be independent and connect to both sensorsand applications while serving as data transceivers between components,software, and—only when required—the cloud. This can provide desirableand reliable constraints for data computation, particularly as sensitivedata can be substantial, often disorganized, and subject toexploitation. Owing to the cloud's limitations for exposure, fogcomputing can provide additional layers of efficiency and security.

By way of example, FIG. 37 illustrates an example systemarchitecture/topology for implementing fog data processing in accordancewith some implementations of the disclosure. The system architectureincludes IoT sensors 3710, edge layer 3720 including edge nodes 3721,fog layer 3730 including fog nodes 3731, and cloud layer 3740 includingone or more cloud computing devices 3741. Although FIG. 37 will beprimarily described in context of a system architecture as applied to asingle user/wearer, it should be appreciated that this systemarchitecture can be extended to multiple independent users.

IoT sensors 3710 can be sensors implemented as part of a wearable device(e.g., wearable device 10 or wearable device 500. For example, thesensors can include a pupillometry sensor 204, a galvanic skin sensor205, an inertial movement unit 206, a temperature sensor 309, an audiosensor 309, an image sensor (e.g., as part of camera 550), etc. The IoTsensors 3710 can also include sensors that are in the same environmentas the wearer but implemented in a different device. For example, thesensors can include sensors implemented in a mobile device 20 (e.g., GPSor motion sensors), ambient temperature sensors, image sensors ofexternal IoT devices, audio sensors of external IoT devices, etc.

Edge nodes 3721 and fog nodes 3731 can be implemented in hardwareincluding, but not limited to, client-side wearable devices (e.g.,wearable device 10 or 500), a mobile device 20, and/or locally (i.e.,pre-cloud) operated servers or database devices that can be provided bythe provider of the wearable device system.

As depicted, the fog layer 3730 resides between the edge layer 3720 andcloud layer 3740. In some implementations, the edge nodes can residebetween the cloud nodes and fog nodes. In some implementations, someedge nodes reside between cloud nodes and fog nodes, and some fog nodesreside between edge nodes and cloud nodes. As fog node(s) 3731 receivedata from edge node(s) 3721 they can filter the data bydeterministically passing only appropriate data to the cloud computingdevices 3741 for processing, storage, networking, etc. For example, edgeand fog computations can be implemented where an ecological parameter(e.g., temperature) or physiological parameter (e.g., heart rate) isregularly sensed and collected as data (e.g., every second of operation)to align user fatigue and anxiety with other ecological/physiologicalmeasures. Without the presence of fog layer 3730, every sensormeasurement could potentially be transmitted to a cloud application toaccommodate the user/wearer and downstream monitoring for therapists,general practitioners, and/or family members. A rules-based fog layer3730 could prevent this excessive data transfer from congesting thenetwork and/or compromising the user's privacy/security. For example, afog node 3731 can be configured to pass only critical data as it occurs(e.g., excessive temperature spikes), or only data collected by certainsensors (e.g., no image or sound data is made available to the cloud).

The fog node(s) 3731 or edge nodes 3721 can also encrypt any data priorto making it available to a cloud computing device 3741 such thatinformation can remain pseudonymized, thereby protecting the user'sprivacy. During operation, all encryption, decryption, and purging ofdata can take place locally at the user level and not using cloudsoftware or hardware.

The edge nodes 3721 can be responsible for maintaining a middlewareposition that manages data flow, encryption/decryption, and ultimatelyexpunging data once it is no longer needed. In some implementations, thesame device can function as both a fog node and an edge node.

By virtue of localizing data management and processing in the “fog”,various benefits can be realized by the stakeholders (e.g., user) and/orthe wearable device system. First, data processing near the end-user canimprove data access, allowing storage to be buffered away fromexpensive, inefficient, and insecure/unethical activities. For example,this can prevent data collection for marketing or sale. “Fogging”, byfocusing data processing at the wearable device or within a localnetwork of the wearable device can also bypass low-speed connectivity,i.e., expensive and slower cloud transmission rates are not required fora grounded application. Additionally, by localizing datamanagement/processing/storage, data protection and privacy rules can becontrolled and managed by the user, allowing configuration of what canand cannot be collected, transmitted, and/or stored. For example,localization can occur within a LAN and/or ad-hoc network of thewearable device (e.g., 10) and/or mobile device (e.g., 20) coupled tothe wearable device.

In some implementations, a cloud layer 3740 can include a data lake (DL)repository that stores machine learning (ML) data that is notpersonalized, including images, audio, and/or video. Some or most ofthis data can be public domain. During operation, the fog layer 3730 cancompare private and distracting conditions (e.g., as determined fromdata collection by the wearable device) to the data stored in the cloudlayer 3740. In this configuration, the edge layer 3720 can coordinatedata flows to the cloud layer 3740, only allowing the most limited flowto the cloud, while the fog layer 3740 can be used to detectdistractions by the user based at least in part by the repository ofdata stored on the cloud layer 3740. As such, the system can operatewithout the cloud layer personally identifying a user.

In some implementations, all personalized user data, includingthresholds, sensory resolutions, mediations, demographic data,diagnostic data, etc. can be stored at the local level. Deep learningand machine learning data (e.g., auditory, visual, etc.) distractibilitydata can be encrypted and stored globally, while real-time comparativereactivity to ecological and physiological data can be momentarilystored locally.

FIG. 38A depicts a particular example of a wearable system architecture,including data flows, that leverages fog and edge computing, inaccordance with some implementations of the disclosure. FIG. 38B is aflow diagram illustrating operations that are performed by the system ofFIG. 38A, in accordance with some implementations.

The system of FIG. 38A includes a wearable device 10, one or more edgeservices 3810, fog services 3820, gateway 3830 that can mediatecommunication between edge server 3810 and fog services 3820, and one ormore cloud computing devices 3840. In some implementations, thefunctionalities of edge server 3810 can be implemented in wearabledevice 10 or a mobile device 20 communicatively coupled to wearabledevice 10.

Operation 3901 includes wearable device 10 collecting sensor data. Forexample, one or more sensors of the wearable device 10 can be used torecord a sensory input stimulus to the user. This can include sensingecological and physiological/psychophysiological data as describedabove. In some implementations, other devices besides wearable device10, but in the same environment as wearable device 10 (e.g., a mobiledevice 20), and collect sensor data.

Operation 3902 includes one or more fog nodes of fog services 3820processing, storing, and/or managing the sensor data that was collected.In some implementations the one or more fog nodes include a datastorethat stores and/or manages the sensor data. In some implementations, theone or more fog nodes include a datastore that stores one or moresensory thresholds specific to a user of the wearable device 10 (e.g.,one or more sensory thresholds selected from auditory, visual, orphysiological sensory thresholds). In some implementations, the one ormore fog nodes compare the sensory input stimulus with the one or moresensory thresholds specific to the user to determine that anintervention could be required.

Operation 3904 includes the edge server(s) 3810 encrypting and uploadingdata to the one or more cloud computing devices 3840. For example, ifthe fog services 3820 determined, after reviewing a subset of sensordata, that a threshold has been met, this subset of sensor data thattrigged the determination can be encrypted by edge server 3810 anduploaded to the cloud.

Operation 3905 includes applying a data processing and machine learningpipeline/process. The pipeline can be performed using at least one ormore cloud computing devices. Operation 3907 includes presenting anintervention/mediation to the user. As an example, a user can bevisually distracted, which triggers changes in pupillary measurements.The updated pupillary measurements can result in a threshold being metthat causes a mediation/intervention (e.g., alert to the user torefocus) to be presented to the user. The mediation can be triggered asfollows. At the time of the pupillary measurement, an outward facingcamera (e.g., as incorporated in a wearable device) captures an image ofan object causing the distraction (e.g., the camera captures an image inthe direction of the pupillary gaze). If the image matches or issufficiently similar to (e.g., as determined by calculating a similarityscore based on image features) a publicly stored image on the cloud ofthe same/similar object that was previously tagged as a personalizedtrigger for the user as a distracting cue, the mediation can betriggered. The machine learning pipeline can be used to match thecaptured image to the cloud's data store, and the image can be confirmedup and downstream as a distracting image. To make the comparison can beprocessed based on different parameters, including color, shape, edgedetection, etc.

In some implementations, an FBDL model can be used to generatecustomized mediations given data from one or more sensors as inputs.Deep learning is a machine learning category that uses neural networkalgorithms that memorialize data for analysis and prediction. Neuralnetworks use hidden layers to obtain features by connecting one anotherfor replicable outcomes (output layers). FBDL confines connectionsbetween input and hidden layers so that every veiled unit attaches to asub-section of its corresponding input. Hence, lower dimensionedcharacteristics can be derived by arbitrarily sampling big data. FIG. 47illustrates one example of a FBDL model, in accordance with someimplementations of the disclosure. As depicted, inputs can be one ormore different types of sensor data, including audio data, pupillarydata, IMU data, GSR data, optical data (e.g., image data), temperaturedata, etc. Through multiple layers the FBDL can be trained to recognize,based on the input data, a particular/personalized mediation typedepending on recognition, where the mediation can be an alert, filter,guidance, or combination thereof.

Open Learner Model

Various implementations of the technology described herein can leveragean OLM to graphically represent (e.g., using a GUI) the current progressof users of multi-sensory, assistive wearable technology (e.g.,neurodiverse individuals) such that the users or other interested party(e.g., therapist) can visualize, track, and/or reflect on theirprogress. OLM components can be incorporated into the systems andmethods described herein to improve individual's hyper-, hypo-, andsensory-seeking challenges, which may affect task accuracy (i.e.,performance), and mental health (i.e., calmness and alertness),particularly when distracted by eco or psychophysiological cues.Mediations that are fully transparent can provision results better thanthose that limit user's data access, straightforward system control,confidence, and trust in technologies.

While the majority of OLM data collected and used in some conventionaltechnologies is fully exposed (e.g., disclosed) to a large number ofindividuals, with minimal safeguards for security and privacy, thetechnology described herein incorporates OLM in a system designed topromote security, privacy, and efficient use of computational resources,including bandwidth and storage. For example, the systems and methodsdescribed herein can utilize OLM in tandem with fog and edge networkingas discussed above. Additionally, the GUI associated with the OLM modelcan be used to set access controls.

FIGS. 39-45 depict an OLM framework in accordance with someimplementations of the disclosure. The depicted OLM framework includesthree tables (FIGS. 40, 42, and 44 ) and four flowcharts (FIGS. 39, 41,43, and 45 ). The OLM framework depicts custom labeled characteristicspertinent to securing data by the individual user/wearer and theirsupport (e.g., therapist, family, etc.) During operation, a wearer mayselect/actuate controls on a GUI to determine how little or how muchdata can be sensed, collected, processed, and/or shared on afeature-by-feature basis. As depicted, characteristics or features canbe divided into what elements are important and to be sensed, mediated,and/or stored, how this is accomplished, and access privileges forreviewing and administering these functions.

FIG. 39 is a high-level flowchart of the OLM framework. This example OLMframework defines eleven elements (i.e., model accessibility,presentation, access method, accessibility control, etc.) within threecategories, their corresponding properties (i.e., complete, partial,current, future, etc.), and their description (i.e., a textualexplanation of each purpose element) used in defining the specific OLM(See tables of FIGS. 40, 42, 44 , left to right). These propertiessignify levels of accessibility purpose elements across eleven aspectcolumns (i.e., from left to right including right to access, controlthrough trust, assessment, etc.). Each aspect uses ranking levels todifferentiate elements from one another (i.e., those deemed critical orespecially critical are marked X and XX, respectively; those deemeddebatable are marked=; and those not relevant are left blank).

One of the OLM maps describes “what is available” (FIGS. 40-41 ) byaddressing the extent of model accessibility, underlyingrepresentations, access to uncertainty, role of time, access to sourceissues, and access to model personalization. By way of example, themodel's extent of accessibility (Item #1) is predominantly open“Completely” across the board with critical availability to nearly allstakeholders.

One of the OLM maps describes “how the model is presented” tostakeholders, including friends and acquaintances (FIGS. 42-43 ).Included are presentation details (i.e., word cloud, skill meters, radarplots, etc.), access methods (i.e., inspectable, editable, user versussystem persuasion, etc.), and access flexibility. Compared to the “whatis available” table, this table include elements tagged with criticaland especially critical rankings.

One of the OLM maps describes “who controls access” (FIGS. 44-45 )discloses two purpose elements that map focal points (i.e., whomaccessibility is derived from) and dominant access (i.e., who controlsaccess over others).

Some implementations of the multi-sensory assistive wearable technologydescribed herein can leverage an AR-supported framework of development,analysis, and assessment criteria. In this case, the AR support canrefer to the use of AR to sonically or visually replace certain auditoryor visual information presented to the user, such as, for example,blurring, squelching, or erasing an offending image, or performingdigital signal processing of an audio signal to make it lessdistracting. The framework can provide a mechanism for implementingimproved OLM, quantified self (QS) frameworks, and/or multimodallearning analytic (MMLA) frameworks. To this end, FIG. 46 depicts asystem that implements an AR-based MMLA framework, in accordance withsome implementations of the disclosure. As depicted, the system isconfigured to implement at least three functions for the user and/orother stakeholders of the multi-sensory assistive wearable technologydescribed herein: battery, diagnoses, and personalization; objectives,aims, and iterative outcomes; and mediative strategy and digitalaccommodations via technology (e.g., using multimodal sensors andimplementing intervention strategies). Depending on configured accesscontrols, stakeholders including users can maintain accessibilitythroughout the framework

As depicted by FIG. 46 , general practitioners, therapists, instructors,employers, caregivers, and/or educators can help guide theaforementioned three functions. Sensory sensitivity, attention, mentalhealth objectives, aims, and iterative outcomes can be augmented usingAR augmentation and/or OLM applications. The individual can be aneurodiverse or potentially neurodiverse individual who can be at-riskin social settings, an employee at work, and/or student in a highereducation institution venue. Battery and diagnostics (i.e., MaRs-IB,ASRS, AQ-50, etc.) including individual sensitivity and mediationprofiles (ISIP) can be developed for each user. As described herein, anISIP can refer to a data profile provisioned by one or more mobileapplication(s) and used daily by a user to customize their reactivityand provide behavior modification (e.g., using the DistractionIntervention Desire Questionnaire). An ISIP can help personalizeuser-specific sensory thresholds and/or sensory resolutions, describedherein, that can affect alert, filter, and guidance interventionsprovided for a given user.

In some implementations, ISIPs can utilize state-based anxiety andfatigue monitoring (SAFE) and randomized, regular feedback (FADE) toensure ethical compliance, efficacy, and user satisfaction. Data can bestored on wearable devices (e.g., wearable devices 10) mobile devices(e.g. mobile device 20), and/or other devices within a LAN or ad-hocnetwork of the wearable device/mobile device, and available for OLMapplication parsing or stakeholder review. Owing to the data'ssensitive, contextual and personalized nature, the majority ofinformation can localized, and processed-wherever possible-only usingedge and fog transmission as described above with reference to FIGS. 37and 38A-38B. Cloud processing, transmission, and storage can beminimized or avoided entirely to preserve privacy/security and ensureethical robustness. Further security can be enabled throughencryption/decryption policies that provide additional safeguardinglayers whenever stakeholders review or process sensitive data.

FIG. 48 is a high level flow diagram conceptually illustrating theoperation of a multi-sensory assistive wearable system, in accordancewith some implementations of the disclosure. An individual/wearer reactsto the environment. MMLA sensors (e.g., as incorporated in the wearabledevice and/or some other device in the user's environment) collect datacorresponding to ecological cues (e.g., temperature data, image data,etc.) and psychophysiological cues (e.g., pupillary data, GSR data,heart rate data, etc.). The user's sensitivity profile, which caninclude thresholds and intervention/mediation preferences, is used todetermine an intervention/media that is an alert, filter, guidance, orcombination thereof. The user's reaction continues to be measured afterthe mediation. As such, a feedback loop can enable a constant andconsistent pathway to unfold, whereby the individual's responses areweighed against ecological and psychophysiological responses. Once apersonalization threshold is exceeded, an assistive or mediative eventoccurs, and the system again monitors the individual's response-weighingthis against the current sensory input.

In some implementations, as long as the system determines that there isa mismatch in signals that align to personalization aims that can beaccommodated through assistive means, and that these address sensory,attention, and/or mental health markers, the individual can receive amediation. In some implementations, if mediations are no longereffective or not enhancing a user's experience, they can be disabled bythe individual (e.g., via a user interface of the wearable device ormobile device) or any of the stakeholders.

In some implementations, cues that are no longer distracting can beremoved from the identification process.

Tables 47A-47B show some example design specifications, includinglatency parameters, for implementing audiometric sensing,physiological/psychophysiological sensing, and transmission inaccordance with some implementations of the disclosure. It should beappreciated that system specifications can vary depending on theavailable hardware.

TABLE 47A (design specifications for low performance) ProtocolDescription Range Latency Bitrate Audiometric sensing Omnidirectional 50Hz-20 kHz 11.61-23.22 ms 512-1024 dynamic or response; −42 to −30samples @ 44.1 moving coil dBv sensitivity, S/N kHz sampling microphone60 dBA, and 2 KΩ rate output Physiological/ GSR SCL 2-20 μS; SCR 1-3 s;Frequency 1- Psychophysiological conductance Change in SCL 1- SCR risetime 3pm sensing and triaxial 3 μS; 1-3 s; SCR half accelerometerAmplitude 0.2-1 μS; recovery time 2-10 s Bluetooth Headset 5-30 meters  200 ms  2.1 Mbps  transmission wearable to mobile phone Wireless 32mindoors ~150 ms 600 Mbps transmission 95m outdoors

TABLE 47B (design specifications for enhanced performance) ProtocolDescription Range Latency Bitrate Audiometric sensing Omnidirectional 20Hz-20 kHz 2.9-5.8 ms 128-256 dynamic or moving response; −42 samples @coil microphone dBv sensitivity, 44.1 kHz S/N 39 dBA, and sampling 1 KΩoutput rate Physiological/ GSR conductance and SCL 2 μS; SCR 1 s; SCRFrequency Psychophysiological triaxial accelerometer Change in SCL risetime 1 s; 3pm sensing 1 μS; SCR half Amplitude recovery time 0.2 μS; 2 sBluetooth Headset wearable to 30 meters   200 ms  2.1 Mbps  transmissionmobile phone or computer Wireless Mobile or computer 32m indoors ~150 ms600 Mbps transmission to router 95m outdoors

Applications

The multi-sensory assistive wearable technology described herein can beutilized across a myriad of applications to supply a myriad of potentialadvantages. For example, in an employment application, the technologydescribed herein can potentially reduce distractibility, improveattention and performance, lower anxiety, and/or increase employeeoutput and/or satisfaction. Metrics that could potentially be improvedin the employment application include improved onboarding and trainingof neurodiverse, autistic, and neurotypical applicants and new hires,reduced employee turnover, increased productivity rate, diversity and/orinclusion, increased profit per employee, lowered healthcare costs,and/or ROI, employee net promoter score, cost of HR per employee,employee referral, combinations thereof and the like.

In an academic application, the technology described herein canpotentially increase concentration and/or comprehension, and reduced,minimized and/or substantially eliminated hesitation and/or increased,enhanced and/or increased comfort. Metrics that could potentially beimproved in an academic application include retention rates (next termpersistence versus resignation), graduation rates, time to completion,credits to degree and/or conferrals, academic performance, educationalgoal tracking, academic reputation, and/or underemployment of recentgraduates.

In a social application, the technology described herein can potentiallyincrease participation and/or motivation, and reduce apprehension.Metrics that could potentially be improved in a social applicationinclude primary socialization (learn attitudes, values, and/or actionsappropriate to individuals and culture), secondary socialization (learnbehavior of smaller groups within society), developmental socialization(learn behavior in social institution and/or developing social skills),anticipatory socialization (rehearse future positions, occupations,and/or relationships), and resocialization (discarding former behaviorand/or accepting new patterns as part of transitioning one's life).

In a transportation lorry/trucking application, the technology describedherein can potentially increase and/or improve attention and/orperformance, reduce fatigue, and improve response times. Metrics thatcould potentially be improved in a transportation lorry/truckingapplication include logistics benefits including increased safety and/orproductivity (shut down engine, recommend rest, crash data statisticsand/or analysis, etc.), reduced logistical strain and/or financialburden (reduced shipping, delivery time, and/or transportation costs),effective planning, dispatch, and/or scheduling.

In a transportation aircraft application, the technology describedherein can potentially increase focus and/or performance, and reducefatigue and/or apprehension reduction. Metrics that could potentially beimproved in a transportation aircraft setting include safety (e.g.,fatality and/or accident rate, system risk events, runway incursions,hazard risk mitigation, commercial space launch incidents, world-widefatalities), efficiency (taxi-in/out time, gate arrival/delay,gate-to-gate times, distance at level-flight descent, flown v. filedflight times, average distance flown, arrival and/or departure delaytotals, number of operations, on-time arrivals, average fuel burned),capacity (average daily capacity and daily operations, runway pavementconditions, NAS reliability), environment (noise exposure, renewable jetfuel, NAW-wide energy efficiency, emission exposure), and/or costeffectiveness (unit per cost operation).

In an IoT application, the technology described herein can potentiallyintegrate mechanical and digital machines, objects, animals, and/orpeople (each with unique identifiers) received transferred informationfrom the wearable so that actionable commands and/or analyses can occur.Metrics that could potentially be improved in the IoT applicationinclude an increase in physiological/psychophysiological activity canprovide alerts to parents, caregivers, and/or professionals (para andotherwise) in the event wearable thresholds are exceeded. Integration toenvironmental control units (ECU) bridge between the wearable andappliances including, but not limited to TV's, radios, lights, VCR's,motorized drapes, and/or motorized hospital beds, heating, and/orventilation units (air-con), clothes washers and/or driers.

In a performance enhancement application, the technology describedherein can potentially improve procrastination, mental health, fatigue,anxiety, and/or focus. Metrics that could potentially be improved in aperformance enhancement application include testing (logic processing,advocacy, curiosity, technical acumen and/or tenacity), Leadership(mentorship, subject matter expertise, team awareness, interpersonalskills, reliability), Strategy & Planning (desire, quality, community,knowledge and functionality), Intangibles (communication, diplomacy,negotiations, self-starter, confidence, maturity and selflessness).

In telemedicine, emergency medicine, and healthcare application, thetechnology described herein can potentially improve the ability formedical and healthcare practitioners to share data with wearable usersto help fine tune therapies, Rx, dispatch for emergency assist, surgicalsuite monitoring and/or optimization, work-schedule, and/or logisticsstrategy, pupillometry indicating unsafe conditions, unsafe warnings ifthresholds are crossed (performance orphysiological/psychophysiological). Metrics that could potentially beimproved in a telemedicine, emergency medicine, and/or healthcareapplication include telemedicine metrics (e.g., consultation time,diagnoses accuracy, rate of readmission, quality of service/technology,patient and/or clinician retention, time and/or travel saved, treatmentplan adherence, patient referral), surgical metrics (e.g., first casestarts, turnover times, location use/time, complications, value-basedpurchasing, consistency of service, outcomes), emergency metrics (e.g.,average patient flow by hour, length of processing/stay,Time-to-Relative Value Unit, Patients Seen, RVU produced, CurrentProcedural Terminology (CPTs) performance, average evaluation andmanagement distribution percentage, total number of deficient charts.

In a parental, guardian, and/or educational monitoring application, thetechnology described herein can potentially abet metric parenting (andguardianship) whereby work-life balance is made possible by meetingactionable and measurable goals and deadlines to improve familydynamics, including being more present, aware, and/or trackingengagement of children (particularly those with exceptionalities,although it is not limited to gifted, neurodiverse but all children).Metrics that could potentially be improved in a parental, guardian,and/or educational monitoring application include family time,engagement, academic improvement, reduction in digital mediatechnologies, screen time, online and console gaming, scheduleadherence, nutritional faithfulness, safety and/or exposure to substanceabuse, seizure and/or location monitoring.

As various changes could be made in the above systems, devices andmethods without departing from the scope of the invention, it isintended that all matter contained in the above description shall beinterpreted as illustrative and not in a limiting sense. Any numbersexpressing quantities of ingredients, constituents, reaction conditions,and so forth used in the specification are to be interpreted asencompassing the exact numerical values identified herein, as well asbeing modified in all instances by the term “about.” Notwithstandingthat the numerical ranges and parameters setting forth, the broad scopeof the subject matter presented herein are approximations, the numericalvalues set forth are indicated as precisely as possible. Any numericalvalue, however, may inherently contain certain errors or inaccuracies asevident from the standard deviation found in their respectivemeasurement techniques. None of the features recited herein should beinterpreted as invoking 35 U.S.C. § 112, paragraph 6, unless the term“means” is explicitly used.

In this document, the terms “machine readable medium,” “computerreadable medium,” and similar terms are used to generally refer tonon-transitory mediums, volatile or non-volatile, that store data and/orinstructions that cause a machine to operate in a specific fashion.Common forms of machine-readable media include, for example, a harddisk, solid state drive, magnetic tape, or any other magnetic datastorage medium, an optical disc or any other optical data storagemedium, any physical medium with patterns of holes, a RAM, a PROM,EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge, andnetworked versions of the same.

These and other various forms of computer readable media can be involvedin carrying one or more sequences of one or more instructions to aprocessing device for execution. Such instructions embodied on themedium, are generally referred to as “instructions” or “code.”Instructions can be grouped in the form of computer programs or othergroupings. When executed, such instructions can enable a processingdevice to perform features or functions of the present application asdiscussed herein.

In this document, a “processing device” can be implemented as a singleprocessor that performs processing operations or a combination ofspecialized and/or general-purpose processors that perform processingoperations. A processing device can include a CPU, GPU, APU, DSP, FPGA,ASIC, SOC, and/or other processing circuitry.

The various embodiments set forth herein are described in terms ofexemplary block diagrams, flow charts and other illustrations. As willbecome apparent to one of ordinary skill in the art after reading thisdocument, the illustrated embodiments and their various alternatives canbe implemented without confinement to the illustrated examples. Forexample, block diagrams and their accompanying description should not beconstrued as mandating a particular architecture or configuration.

Each of the processes, methods, and algorithms described in thepreceding sections can be embodied in, and fully or partially automatedby, instructions executed by one or more computer systems or computerprocessors comprising computer hardware. The processes and algorithmscan be implemented partially or wholly in application-specificcircuitry. The various features and processes described above can beused independently of one another, or can be combined in various ways.Different combinations and sub-combinations are intended to fall withinthe scope of this disclosure, and certain method or process blocks canbe omitted in some implementations. Additionally, unless the contextdictates otherwise, the methods and processes described herein are alsonot limited to any particular sequence, and the blocks or statesrelating thereto can be performed in other sequences that areappropriate, or can be performed in parallel, or in some other manner.Blocks or states may be added to or removed from the disclosed exampleembodiments. The performance of certain of the operations or processescan be distributed among computer systems or computers processors, notonly residing within a single machine, but deployed across a number ofmachines.

As used herein, the term “or” can be construed in either an inclusive orexclusive sense. Moreover, the description of resources, operations, orstructures in the singular shall not be read to exclude the plural.Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps.

Terms and phrases used in this document, and variations thereof, unlessotherwise expressly stated, should be construed as open ended as opposedto limiting. Adjectives such as “conventional,” “traditional,” “normal,”“standard,” “known,” and terms of similar meaning should not beconstrued as limiting the item described to a given time period or to anitem available as of a given time, but instead should be read toencompass conventional, traditional, normal, or standard technologiesthat may be available or known now or at any time in the future. Thepresence of broadening words and phrases such as “one or more,” “atleast,” “but not limited to” or other like phrases in some instancesshall not be read to mean that the narrower case is intended or requiredin instances where such broadening phrases may be absent.

We claim:
 1. A system, comprising: a wearable device comprising one ormore sensors; one or more processors; and one or more non-transitorycomputer-readable media having executable instructions stored thereonthat, when executed by the one or more processors, cause the system toperform operations comprising: obtaining user sensory sensitivity datacorresponding to user input indicating whether a user of the wearabledevice is visually sensitive, sonically sensitive, or interoceptivelysensitive; determining, using at least the user sensory sensitivitydata, one or more sensory thresholds specific to the user and mediationdata corresponding to one or more mediations specific to the user, theone or more sensory threshold selected from auditory, visual, orphysiological sensory thresholds; storing the one or more sensorythresholds and the mediation data; recording, using the one or moresensors, a sensory input stimulus to the user; comparing the sensoryinput stimulus with the one or more sensory thresholds specific to theuser; in response to comparing the sensory input stimulus with the oneor more sensory thresholds, determining, based at least on the mediationdata, a mediation to be provided to the user, the mediation configuredto provide the user relief from distractibility, inattention, anxiety,fatigue, or sensory issues; and providing the mediation to the user, themediation comprising an alert mediation, a guidance mediation, or afilter mediation.
 2. The system of claim 1, wherein: the operationsfurther comprise: storing a first identifier that indicates whether theuser is neurodiverse or neurotypical; and determining the one or moresensory thresholds specific to the user and the mediation datacorresponding to one or more mediations specific to the user, comprises:determining, using at least the first identifier and the user sensorysensitivity data, the one or more sensory thresholds and the mediationdata.
 3. The system of claim 2, wherein: the operations furthercomprise: receiving user demographic data corresponding to user inputindicating an age, education level, or gender of the user; anddetermining the one or more sensory thresholds specific to the user andthe mediation data corresponding to one or more mediations specific tothe user, comprises: determining, using at least the first identifier,the user sensory sensitivity data, and the user demographic data, theone or more sensory thresholds and the mediation data.
 4. The system ofclaim 2, wherein the first identifier indicates whether or not the useris autistic.
 5. The system of claim 4, wherein the first identifierindicates that the user is autistic.
 6. The system of claim 5, wherein:the mediation is configured to provide the user relief from fatigue; themediation comprises the filter mediation; and the filter mediationcomprises filtering, in real-time, an audio signal presented to the useror an optical signal presented to the user.
 7. The system of claim 5,wherein the mediation is configured to provide the user relief from adistraction by increasing a response time of the user to thedistraction.
 8. The system of claim 5, wherein: obtaining the usersensory sensitivity data comprises receiving, at a graphical userinterface, one or more first responses by the user to one or more firstprompts indicating whether the user is visually sensitive, sonicallysensitive, or interoceptively sensitive; and the operations furthercomprise deriving the first identifier indicating that the user isautistic by: receiving, at the graphical user interface, one or moresecond responses by the user to one or more second prompts indicating ananxiety level of the user; deriving, based on the sensory sensitivitydata, one or more sensory sensitivity scores comprising a visualsensitivity score, a sonic sensitivity score, or an interoceptivesensitivity score; deriving, based on the one or more second responses,an anxiety score; and predicting, using a model that predicts aprobability of autism based at least on an anxiety level and one or moresensory sensitivity levels, based at least on the anxiety score and theone or more sensory sensitivity scores, that the user is autistic. 9.The system of claim 5, wherein: obtaining the user sensory sensitivitydata comprises receiving, at a graphical user interface, one or morefirst responses by the user to one or more first prompts indicatingwhether the user is visually sensitive, sonically sensitive, orinteroceptively sensitive; and the operations further comprise derivingthe first identifier indicating that the user is autistic by: receiving,at the graphical user interface, one or more second responses by theuser to one or more second prompts indicating a fatigue level of theuser; deriving, based on the sensory sensitivity data, one or moresensory sensitivity scores comprising a visual sensitivity score, asonic sensitivity score, or an interoceptive sensitivity score;deriving, based on the one or more second responses, a fatigue score;and predicting, using a model that predicts a probability of autismbased at least on a fatigue level and one or more sensory sensitivitylevels, based at least on the fatigue score and the one or more sensorysensitivity scores, that the user is autistic.
 10. The system of claim1, wherein obtaining the user sensory sensitivity data furthercomprises: recording, using at least the one or more sensors, a responseby the user to a visual stimulus, a sonic stimulus, or a physiologicalstimulus.
 11. The system of claim 1, wherein the mediation comprises acombination mediation of at least two mediations selected from the alertmediation, the guidance mediation, and the filter mediation.
 12. Thesystem of claim 11, wherein the combination mediation comprises thealert mediation followed by the filter mediation.
 13. The system ofclaim 12, wherein: the alert mediation comprises alerting the user abouta distraction that is visual or auditory; and the filter mediationcomprises: comprising filtering, in real-time, an audio or opticalsignal presented to the user, the audio or optical signal associatedwith the distraction.
 14. The system of claim 1, wherein the systemfurther comprises one or more fog nodes configured to locally storesensor data collected by the one or more sensors, the sensor dataincluding first sensor data associated with the sensory input stimulus.15. The system of claim 14, wherein: storing the one or more sensorythresholds and the mediation data, comprises: locally storing, using theone or more fog nodes, the one or more sensory thresholds and themediation data; and comparing the sensory input stimulus with the one ormore sensory thresholds, comprises: comparing, using the one or more fognodes, the sensory input stimulus with the one or more sensorythresholds.
 16. The system of claim 14, further comprising one or moreedge nodes configured to communicatively couple to the one or more fognodes and a cloud server remotely located from the wearable device. 17.The system of claim 16, wherein the one or more edge nodes areconfigured to: encrypt the first sensor data associated with the sensoryinput stimulus to obtain encrypted data; transmit the encrypted data tothe cloud server; and receive a response from the cloud server.
 18. Thesystem of claim 16, wherein the one or more fog nodes and the one ormore edge nodes reside on a local area network (LAN) containing thewearable device, an ad-hoc network containing the wearable device, a LANof a mobile device directly coupled to the wearable device, or an ad-hocnetwork of the mobile device.
 19. The system of claim 16, wherein: thesensor data comprises second sensor data that does not trigger amediation; and the system is configured such that the second sensor datathat does not trigger a mediation is not made available to any cloudserver remotely located from the wearable device.
 20. The system ofclaim 16, wherein: the mediation comprises the filter mediation thatcomprises filtering, in real-time, an optical signal presented to theuser; the first sensor data associated with the sensory input stimuluscomprises first image data; the one or more edge nodes or the one ormore fog nodes are configured to determine whether the first image datais sufficiently similar to second image data stored at the cloud server;and determining the mediation to be provided to the user comprises inresponse to determining that the first image data is sufficientlysimilar to the second image data, determining the filter mediation. 21.The system of claim 16, wherein: the mediation comprises the filtermediation that comprises filtering, in real-time, an audio signalpresented to the user; the first sensor data associated with the sensoryinput stimulus comprises first audio data; the one or more edge nodes orthe one or more fog nodes are configured to determine whether the firstaudio data is sufficiently similar to second audio data stored at thecloud server; and determining the mediation to be provided to the usercomprises in response to determining that the first audio data issufficiently similar to the second audio data, determining the filtermediation.
 22. The system of claim 1, wherein the operations furthercomprise: presenting to the user, on a graphical user interface, one ormore access controls for controlling user data that is made available toone or more other users, the user data comprising sensor data collectedby the one or more sensors, the one or more sensory thresholds, themediation data, or a record of mediations presented to the user; andreceiving data corresponding to user input selecting the one or moreaccess controls.
 23. The system of claim 1, wherein the operationsfurther comprise: presenting to the user, on a graphical user interface,one or more access controls that grant or deny access to one or moreother users to influence mediations that are presented to the user; andreceiving data corresponding to user input actuating the one or moreaccess controls.
 24. The system of claim 1, wherein the operationsfurther comprise: presenting to the user, on a graphical user interface,a graphical summary of progress of the user from using the wearabledevice, the graphical summary including a moving average of time betweenmediations.
 25. The system of claim 1, wherein: the one or more sensorscomprise multiple sensors of different types, the multiple sensorscomprising: an auditory sensor, a galvanic skin sensor, a pupillarysensor, a body temperature sensor, a head sway sensor, or an inertialmovement unit; recording the sensory input stimulus to the usercomprises obtaining first sensory data corresponding to a first sensoryinput stimulus from a first sensor of the multiple sensors, and secondsensory data corresponding to a second sensory input stimulus from asecond sensor of the multiple sensors; and determining the mediation tobe provided to the user, comprises: inputting at least the first sensorydata and the second sensory data into a fusion-based deep learning(FBDL) model that outputs an identification of the mediation to beprovided to the user.
 26. The system of claim 25 wherein determining themediation to be provided to the user, comprises: inputting at least thefirst sensory data, the second sensory data, and the mediation data intothe FBDL model that outputs the identification of the mediation to beprovided to the user.
 27. A method, comprising: obtaining, at a wearabledevice system, user sensory sensitivity data corresponding to user inputindicating whether a user of a wearable device of the wearable devicesystem is visually sensitive, sonically sensitive, or interoceptivelysensitive; determining, at the wearable device system, using at leastthe user sensory sensitivity data, one or more sensory thresholdsspecific to the user and mediation data corresponding to one or moremediations specific to the user, the one or more sensory thresholdselected from auditory, visual, or physiological sensory thresholds;storing, at a storage of the wearable device system, the one or moresensory thresholds and the mediation data; recording, using one or moresensors of the wearable device system, a sensory input stimulus to theuser; comparing, at the wearable device system, the sensory inputstimulus with the one or more sensory thresholds specific to the user;in response to comparing the sensory input stimulus with the one or moresensory thresholds, determining, based at least on the mediation data, amediation to be provided to the user, the mediation configured toprovide the user relief from distractibility, inattention, anxiety,fatigue, or sensory issues; and providing, using at least the wearabledevice, the mediation to the user, the mediation comprising an alertmediation, a guidance mediation, or a filter mediation.
 28. The methodof claim 27, wherein: the method further comprises: storing a firstidentifier that indicates whether the user is neurodiverse orneurotypical; and determining the one or more sensory thresholdsspecific to the user and the mediation data corresponding to one or moremediations specific to the user, comprises: determining, using at leastthe first identifier and the user sensory sensitivity data, the one ormore sensory thresholds and the mediation data.
 29. The method of claim28, wherein: the method further comprises: receiving user demographicdata corresponding to user input indicating an age, education level, orgender of the user; and determining the one or more sensory thresholdsspecific to the user and the mediation data corresponding to one or moremediations specific to the user, comprises: determining, using at leastthe first identifier, the user sensory sensitivity data, and the userdemographic data, the one or more sensory thresholds and the mediationdata.
 30. The method of claim 28, wherein the first identifier indicateswhether or not the user is autistic.